{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for plotting in notebook\n",
    "%matplotlib inline    \n",
    "\n",
    "# load Python packages\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.formula.api as sm    # for regressions\n",
    "\n",
    "import json\n",
    "import topojson\n",
    "import pyproj  \n",
    "import shapely.ops as ops\n",
    "from shapely.geometry import shape\n",
    "from functools import partial\n",
    "from collections import defaultdict\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Re-run main analysis (figure 2), conditioning on:\n",
    "    - Time of day\n",
    "    - Population density\n",
    "    - Whether strike kills high ranking militant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in drone strike data\n",
    "# subset of 74 strikes was compiled from the Bureau of Investigative Journalism and the New America think tank\n",
    "# see paper for details\n",
    "strikes = pd.read_csv('data_general/drone_strike_data.csv', engine='python')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>new_id</th>\n",
       "      <th>parsed_date</th>\n",
       "      <th>district</th>\n",
       "      <th>governorate</th>\n",
       "      <th>district_id</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>time_start</th>\n",
       "      <th>time_end</th>\n",
       "      <th>civilians_killed_high</th>\n",
       "      <th>civilians_killed_low</th>\n",
       "      <th>militants_killed_high</th>\n",
       "      <th>militants_killed_low</th>\n",
       "      <th>total_killed_high</th>\n",
       "      <th>total_killed_low</th>\n",
       "      <th>rank_militants</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1/12/10</td>\n",
       "      <td>Merkhah As Sufla</td>\n",
       "      <td>Shabwah</td>\n",
       "      <td>2109</td>\n",
       "      <td>14.646474</td>\n",
       "      <td>45.889119</td>\n",
       "      <td>10:40</td>\n",
       "      <td>13:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>1/20/10</td>\n",
       "      <td>Jihanah</td>\n",
       "      <td>Sana'a</td>\n",
       "      <td>2316</td>\n",
       "      <td>15.303591</td>\n",
       "      <td>44.544210</td>\n",
       "      <td>10:40</td>\n",
       "      <td>12:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>1/31/10</td>\n",
       "      <td>Ar Rujum</td>\n",
       "      <td>Al Mahwit</td>\n",
       "      <td>2703</td>\n",
       "      <td>15.363943</td>\n",
       "      <td>43.692904</td>\n",
       "      <td>13:30</td>\n",
       "      <td>15:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>3/14/10</td>\n",
       "      <td>Mudiyah</td>\n",
       "      <td>Abyan</td>\n",
       "      <td>1202</td>\n",
       "      <td>13.931505</td>\n",
       "      <td>46.070609</td>\n",
       "      <td>21:40</td>\n",
       "      <td>3/15/10 3:00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>3/15/10</td>\n",
       "      <td>Mudiyah</td>\n",
       "      <td>Abyan</td>\n",
       "      <td>1202</td>\n",
       "      <td>13.931505</td>\n",
       "      <td>46.070609</td>\n",
       "      <td>10:30</td>\n",
       "      <td>13:00</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   new_id parsed_date          district governorate  district_id   latitude  \\\n",
       "0       1     1/12/10  Merkhah As Sufla     Shabwah         2109  14.646474   \n",
       "1       3     1/20/10           Jihanah      Sana'a         2316  15.303591   \n",
       "2       4     1/31/10          Ar Rujum   Al Mahwit         2703  15.363943   \n",
       "3       5     3/14/10           Mudiyah       Abyan         1202  13.931505   \n",
       "4       6     3/15/10           Mudiyah       Abyan         1202  13.931505   \n",
       "\n",
       "   longitude time_start      time_end  civilians_killed_high  \\\n",
       "0  45.889119      10:40         13:00                      0   \n",
       "1  44.544210      10:40         12:00                      0   \n",
       "2  43.692904      13:30         15:00                      0   \n",
       "3  46.070609      21:40  3/15/10 3:00                      0   \n",
       "4  46.070609      10:30         13:00                     20   \n",
       "\n",
       "   civilians_killed_low  militants_killed_high  militants_killed_low  \\\n",
       "0                     0                    NaN                   NaN   \n",
       "1                     0                    NaN                   NaN   \n",
       "2                     0                    NaN                   NaN   \n",
       "3                     0                    NaN                   NaN   \n",
       "4                     0                    NaN                   NaN   \n",
       "\n",
       "   total_killed_high  total_killed_low rank_militants  \n",
       "0                  2                 1            NaN  \n",
       "1                  2                 2            NaN  \n",
       "2                  0                 0            NaN  \n",
       "3                  3                 2            NaN  \n",
       "4                 20                 7            NaN  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The first thing we need to do is add characteristics to the drone strikes in the dataframe to evaluate the sub-group effects listed above\n",
    "# First, we will add a column to the dataframe that indicates whether the strike occurred in the morning, afternoon, or night\n",
    "# Second, we will  use the district ID to add information about the population density\n",
    "# Third, we will just turn the rank_militants column into an indicator. \n",
    "strikes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/9z/r1lbz_6x5bggmh2909f3gnd00000gp/T/ipykernel_43980/2659937634.py:3: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  strikes['time_start'] = pd.to_datetime(strikes['time_start'], errors='coerce')\n"
     ]
    }
   ],
   "source": [
    "# STRIKE TIME\n",
    "\n",
    "strikes['time_start'] = pd.to_datetime(strikes['time_start'], errors='coerce')\n",
    "\n",
    "\n",
    "# Extract hour\n",
    "strikes['hour'] = strikes['time_start'].dt.hour\n",
    "strikes['hour_from_am'] = strikes['hour'] - 6 # End of Fajr Prayer goes between 5:30 and 6:30 am depending on time of year; This is basically \"hours after end of Fajr Prayer\"\n",
    "\n",
    "# Divide day into three time windows of six hours each\n",
    "# 0-6, 6-12, 12-18, 18-24\n",
    "def time_of_day(hour):\n",
    "    if hour < 8:\n",
    "        return \"morning\"\n",
    "    elif hour < 16:\n",
    "        return \"day\"\n",
    "    else:\n",
    "        return \"evening\"   \n",
    "# Apply function to hour column\n",
    "strikes['time_of_day'] = strikes['hour'].apply(time_of_day)\n",
    "# Convert to ordered categorical variable\n",
    "# strikes['time_of_day'] = pd.Categorical(strikes['time_of_day'], categories=['morning', 'afternoon', 'evening', 'night'], ordered=True)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "strikes['high_ranking'] = strikes['rank_militants'].fillna('').str.contains('High level').astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# STRIKE LOCATION Population Density\n",
    "\n",
    "# UNOCHA Population Data\n",
    "pop = pd.read_excel('data_general/cso_2016_population_projection_sexage_disagreggated.xlsx', \n",
    "                    sheet_name='2016 Population by District', header=[1])\n",
    "pop = pop.iloc[:-1]\n",
    "pop = pop.set_index('District P-Code')\n",
    "\n",
    "# scale 2016 district-level populations down to 2010 levels\n",
    "pop_2010 = 23607000\n",
    "scaler = pop_2010 / pop['TOTAL'].sum()\n",
    "pop.loc[:, 'TOTAL'] = pop.loc[:, 'TOTAL'] * scaler\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Shapefiles\n",
    "\n",
    "# load shapefile data of Yemen's districts for plotting\n",
    "# shapefile data encodes the shapes (geographic boundaries) of an area to allow for map plotting\n",
    "with open('data_general/yem-adm2-json-1.json') as json_file:\n",
    "    jdata = json_file.read()\n",
    "    topoJSON = json.loads(jdata)\n",
    "topoJSON.keys()\n",
    "\n",
    "topo_features = topoJSON['objects']['ADM2']['geometries']\n",
    "scale = topoJSON['transform']['scale']\n",
    "translation = topoJSON['transform']['translate']\n",
    "\n",
    "geoJSON = dict(type='FeatureCollection', features=[])\n",
    "for k, tfeature in enumerate(topo_features):\n",
    "    geo_feature = dict(id=k, type=\"Feature\")\n",
    "    geo_feature['properties'] = tfeature['properties']\n",
    "    geo_feature['geometry'] = topojson.geometry(tfeature, topoJSON['arcs'], scale, translation)    \n",
    "    geoJSON['features'].append(geo_feature)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:278: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  type(ring)(zip(*func(*zip(*ring.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:278: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  type(ring)(zip(*func(*zip(*ring.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:278: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  type(ring)(zip(*func(*zip(*ring.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:278: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  type(ring)(zip(*func(*zip(*ring.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/pyproj/crs/crs.py:141: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  in_crs_string = _prepare_from_proj_string(in_crs_string)\n",
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/shapely/ops.py:276: FutureWarning: This function is deprecated. See: https://pyproj4.github.io/pyproj/stable/gotchas.html#upgrading-to-pyproj-2-from-pyproj-1\n",
      "  shell = type(geom.exterior)(zip(*func(*zip(*geom.exterior.coords))))\n"
     ]
    }
   ],
   "source": [
    "# Calculate Population Density\n",
    "density = {}\n",
    "for feature in geoJSON['features']:\n",
    "    poly = feature['geometry']\n",
    "    district_id = float(feature['properties']['HRPcode'][2:])\n",
    "    district_pop = pop.loc[district_id]['TOTAL']\n",
    "\n",
    "    geom = shape(poly)\n",
    "    geom_area = ops.transform(\n",
    "    partial(\n",
    "        pyproj.transform,\n",
    "        pyproj.Proj(init='EPSG:4326'),\n",
    "        pyproj.Proj(\n",
    "            proj='aea',\n",
    "            lat_1=geom.bounds[1],\n",
    "            lat_2=geom.bounds[3])), geom)\n",
    "    district_area = (geom_area.area / 1000000.)    # area in km^2\n",
    "    \n",
    "    density[district_id] = district_pop / district_area\n",
    "density = pd.Series(density)\n",
    "density.name = 'density'\n",
    "density.index.name = 'district_id'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "strikes = strikes.merge(density, left_on='district_id', right_index=True, how='left')\n",
    "strikes = strikes.rename(columns={0: 'density'})\n",
    "\n",
    "# Create binary variable for above and below median population density\n",
    "strikes['high_density'] = (strikes['density'] > strikes['density'].median()).astype(int)\n",
    "\n",
    "# Create a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Strike Date\n",
    "# Dummy for quantile of the \"strike\" id variable, split into three quantiles\n",
    "# make the strike id numeric \n",
    "strikes['strike_id'] = pd.to_numeric(strikes['new_id'], errors='coerce')\n",
    "strikes['strike_quantile'] = pd.qcut(strikes['strike_id'], 3, labels=False)\n",
    "strikes = strikes.drop(columns=['strike_id'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>new_id</th>\n",
       "      <th>parsed_date</th>\n",
       "      <th>district</th>\n",
       "      <th>governorate</th>\n",
       "      <th>district_id</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>time_start</th>\n",
       "      <th>time_end</th>\n",
       "      <th>civilians_killed_high</th>\n",
       "      <th>...</th>\n",
       "      <th>total_killed_high</th>\n",
       "      <th>total_killed_low</th>\n",
       "      <th>rank_militants</th>\n",
       "      <th>hour</th>\n",
       "      <th>hour_from_am</th>\n",
       "      <th>time_of_day</th>\n",
       "      <th>high_ranking</th>\n",
       "      <th>density</th>\n",
       "      <th>high_density</th>\n",
       "      <th>strike_quantile</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1/12/10</td>\n",
       "      <td>Merkhah As Sufla</td>\n",
       "      <td>Shabwah</td>\n",
       "      <td>2109</td>\n",
       "      <td>14.646474</td>\n",
       "      <td>45.889119</td>\n",
       "      <td>2025-06-11 10:40:00</td>\n",
       "      <td>13:00</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>day</td>\n",
       "      <td>0</td>\n",
       "      <td>12.915622</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>1/20/10</td>\n",
       "      <td>Jihanah</td>\n",
       "      <td>Sana'a</td>\n",
       "      <td>2316</td>\n",
       "      <td>15.303591</td>\n",
       "      <td>44.544210</td>\n",
       "      <td>2025-06-11 10:40:00</td>\n",
       "      <td>12:00</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>day</td>\n",
       "      <td>0</td>\n",
       "      <td>96.506820</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>1/31/10</td>\n",
       "      <td>Ar Rujum</td>\n",
       "      <td>Al Mahwit</td>\n",
       "      <td>2703</td>\n",
       "      <td>15.363943</td>\n",
       "      <td>43.692904</td>\n",
       "      <td>2025-06-11 13:30:00</td>\n",
       "      <td>15:00</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>day</td>\n",
       "      <td>0</td>\n",
       "      <td>264.378056</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>3/14/10</td>\n",
       "      <td>Mudiyah</td>\n",
       "      <td>Abyan</td>\n",
       "      <td>1202</td>\n",
       "      <td>13.931505</td>\n",
       "      <td>46.070609</td>\n",
       "      <td>2025-06-11 21:40:00</td>\n",
       "      <td>3/15/10 3:00</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>evening</td>\n",
       "      <td>0</td>\n",
       "      <td>35.085824</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>3/15/10</td>\n",
       "      <td>Mudiyah</td>\n",
       "      <td>Abyan</td>\n",
       "      <td>1202</td>\n",
       "      <td>13.931505</td>\n",
       "      <td>46.070609</td>\n",
       "      <td>2025-06-11 10:30:00</td>\n",
       "      <td>13:00</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>day</td>\n",
       "      <td>0</td>\n",
       "      <td>35.085824</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   new_id parsed_date          district governorate  district_id   latitude  \\\n",
       "0       1     1/12/10  Merkhah As Sufla     Shabwah         2109  14.646474   \n",
       "1       3     1/20/10           Jihanah      Sana'a         2316  15.303591   \n",
       "2       4     1/31/10          Ar Rujum   Al Mahwit         2703  15.363943   \n",
       "3       5     3/14/10           Mudiyah       Abyan         1202  13.931505   \n",
       "4       6     3/15/10           Mudiyah       Abyan         1202  13.931505   \n",
       "\n",
       "   longitude          time_start      time_end  civilians_killed_high  ...  \\\n",
       "0  45.889119 2025-06-11 10:40:00         13:00                      0  ...   \n",
       "1  44.544210 2025-06-11 10:40:00         12:00                      0  ...   \n",
       "2  43.692904 2025-06-11 13:30:00         15:00                      0  ...   \n",
       "3  46.070609 2025-06-11 21:40:00  3/15/10 3:00                      0  ...   \n",
       "4  46.070609 2025-06-11 10:30:00         13:00                     20  ...   \n",
       "\n",
       "   total_killed_high  total_killed_low  rank_militants  hour  hour_from_am  \\\n",
       "0                  2                 1             NaN    10             4   \n",
       "1                  2                 2             NaN    10             4   \n",
       "2                  0                 0             NaN    13             7   \n",
       "3                  3                 2             NaN    21            15   \n",
       "4                 20                 7             NaN    10             4   \n",
       "\n",
       "  time_of_day  high_ranking     density high_density  strike_quantile  \n",
       "0         day             0   12.915622            0                0  \n",
       "1         day             0   96.506820            1                0  \n",
       "2         day             0  264.378056            1                0  \n",
       "3     evening             0   35.085824            1                0  \n",
       "4         day             0   35.085824            1                0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strikes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load mobility data\n",
    "mobility = pd.read_csv('data_mobility/mobility_daily.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>strike</th>\n",
       "      <th>day</th>\n",
       "      <th>id</th>\n",
       "      <th>mobility</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>15700</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-7</td>\n",
       "      <td>113695</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>-7</td>\n",
       "      <td>209385</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>-7</td>\n",
       "      <td>362258</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>-7</td>\n",
       "      <td>546570</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   strike  day      id  mobility\n",
       "0       1   -7   15700       0.0\n",
       "1       1   -7  113695       0.0\n",
       "2       1   -7  209385       0.0\n",
       "3       1   -7  362258       0.0\n",
       "4       1   -7  546570       0.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the mobility dataframe records the distance travelled by proximal individuals\n",
    "# each day for 7 days before the strike, on the day of the strike, and 21 days after\n",
    "# the strike column records the strike ID, the day column records the number of days to the strike,\n",
    "# the id column records a unique anonymized ID for each individual, and\n",
    "# the mobility column records the daily distance travelled in miles\n",
    "mobility.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>strike</th>\n",
       "      <th>day</th>\n",
       "      <th>id</th>\n",
       "      <th>mobility</th>\n",
       "      <th>new_id</th>\n",
       "      <th>parsed_date</th>\n",
       "      <th>district</th>\n",
       "      <th>governorate</th>\n",
       "      <th>district_id</th>\n",
       "      <th>latitude</th>\n",
       "      <th>...</th>\n",
       "      <th>total_killed_high</th>\n",
       "      <th>total_killed_low</th>\n",
       "      <th>rank_militants</th>\n",
       "      <th>hour</th>\n",
       "      <th>hour_from_am</th>\n",
       "      <th>time_of_day</th>\n",
       "      <th>high_ranking</th>\n",
       "      <th>density</th>\n",
       "      <th>high_density</th>\n",
       "      <th>strike_quantile</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>15700</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1/12/10</td>\n",
       "      <td>Merkhah As Sufla</td>\n",
       "      <td>Shabwah</td>\n",
       "      <td>2109</td>\n",
       "      <td>14.646474</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>day</td>\n",
       "      <td>0</td>\n",
       "      <td>12.915622</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-7</td>\n",
       "      <td>113695</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1/12/10</td>\n",
       "      <td>Merkhah As Sufla</td>\n",
       "      <td>Shabwah</td>\n",
       "      <td>2109</td>\n",
       "      <td>14.646474</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>day</td>\n",
       "      <td>0</td>\n",
       "      <td>12.915622</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>-7</td>\n",
       "      <td>209385</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1/12/10</td>\n",
       "      <td>Merkhah As Sufla</td>\n",
       "      <td>Shabwah</td>\n",
       "      <td>2109</td>\n",
       "      <td>14.646474</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>day</td>\n",
       "      <td>0</td>\n",
       "      <td>12.915622</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>-7</td>\n",
       "      <td>362258</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1/12/10</td>\n",
       "      <td>Merkhah As Sufla</td>\n",
       "      <td>Shabwah</td>\n",
       "      <td>2109</td>\n",
       "      <td>14.646474</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>day</td>\n",
       "      <td>0</td>\n",
       "      <td>12.915622</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>-7</td>\n",
       "      <td>546570</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1/12/10</td>\n",
       "      <td>Merkhah As Sufla</td>\n",
       "      <td>Shabwah</td>\n",
       "      <td>2109</td>\n",
       "      <td>14.646474</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>day</td>\n",
       "      <td>0</td>\n",
       "      <td>12.915622</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   strike  day      id  mobility  new_id parsed_date          district  \\\n",
       "0       1   -7   15700       0.0       1     1/12/10  Merkhah As Sufla   \n",
       "1       1   -7  113695       0.0       1     1/12/10  Merkhah As Sufla   \n",
       "2       1   -7  209385       0.0       1     1/12/10  Merkhah As Sufla   \n",
       "3       1   -7  362258       0.0       1     1/12/10  Merkhah As Sufla   \n",
       "4       1   -7  546570       0.0       1     1/12/10  Merkhah As Sufla   \n",
       "\n",
       "  governorate  district_id   latitude  ...  total_killed_high  \\\n",
       "0     Shabwah         2109  14.646474  ...                  2   \n",
       "1     Shabwah         2109  14.646474  ...                  2   \n",
       "2     Shabwah         2109  14.646474  ...                  2   \n",
       "3     Shabwah         2109  14.646474  ...                  2   \n",
       "4     Shabwah         2109  14.646474  ...                  2   \n",
       "\n",
       "  total_killed_low rank_militants  hour  hour_from_am  time_of_day  \\\n",
       "0                1            NaN    10             4          day   \n",
       "1                1            NaN    10             4          day   \n",
       "2                1            NaN    10             4          day   \n",
       "3                1            NaN    10             4          day   \n",
       "4                1            NaN    10             4          day   \n",
       "\n",
       "   high_ranking    density  high_density strike_quantile  \n",
       "0             0  12.915622             0               0  \n",
       "1             0  12.915622             0               0  \n",
       "2             0  12.915622             0               0  \n",
       "3             0  12.915622             0               0  \n",
       "4             0  12.915622             0               0  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge strike characteristics into mobility data, allowing covariates into main model\n",
    "\n",
    "mobility  = mobility.merge(strikes, left_on = 'strike', right_on = \"new_id\", how = \"left\")\n",
    "mobility.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prep mobility data for event study regression\n",
    "\n",
    "# shift the day index to only include positive values for the regression\n",
    "shift = 8\n",
    "mobility['day'] = mobility['day'] + shift\n",
    "\n",
    "# add lead/lag indicator variables corresponding to the day index\n",
    "xs = pd.get_dummies(mobility['day'], prefix='X')\n",
    "df = mobility.merge(xs, left_index=True, right_index=True)\n",
    "\n",
    "# Ensure X columns are numeric, not boolean\n",
    "for col in df.columns:\n",
    "    if col.startswith(\"X_\"):\n",
    "        df[col] = df[col].astype(int)\n",
    "\n",
    "# convert mobility in miles to kilometers\n",
    "km_scalar = 1.60934    # scalar to convert miles to kilometers\n",
    "df['mobility'] = df['mobility'] * km_scalar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_1 + X_2 + X_3 + X_4 + X_5 + X_7 + X_8 + X_9 + X_10 + X_11 + X_12 + X_13 + X_14 + X_15 + X_16 + X_17 + X_18 + X_19 + X_20 + X_21 + X_22 + X_23 + X_24 + X_25 + X_26 + X_27 + X_28 + X_29\n"
     ]
    }
   ],
   "source": [
    "# create string that includes all the lead/lag indicator variables\n",
    "# this string will be used in the statsmodel regression formula\n",
    "# drop the -2 day variable to avoid multicollinearity\n",
    "\n",
    "var = []\n",
    "for i in range(-7, 21+1):\n",
    "    if i == -2:\n",
    "        continue\n",
    "    var.append('X_' + str(i+shift))\n",
    "var_form = ' + '.join(var)\n",
    "\n",
    "print(var_form)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure S13A Mobility Results with Pop. Density Subgroups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create separate dataframes for strikes that occur in above and below median population density\n",
    "df_high = df[df['high_density'] == 1]\n",
    "df_low = df[df['high_density'] == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>mobility</td>     <th>  R-squared:         </th>  <td>   0.025</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.025</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>     nan</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 21 May 2025</td> <th>  Prob (F-statistic):</th>   <td>   nan</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>11:31:37</td>     <th>  Log-Likelihood:    </th> <td>-1.5556e+07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>2665737</td>     <th>  AIC:               </th>  <td>3.111e+07</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>2665673</td>     <th>  BIC:               </th>  <td>3.111e+07</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    63</td>      <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>       <td>cluster</td>     <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>           <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[3]</th>   <td>   23.5447</td> <td>    0.537</td> <td>   43.806</td> <td> 0.000</td> <td>   22.491</td> <td>   24.598</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[4]</th>   <td>   28.9998</td> <td>    0.530</td> <td>   54.747</td> <td> 0.000</td> <td>   27.962</td> <td>   30.038</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[5]</th>   <td>   28.0745</td> <td>    0.535</td> <td>   52.461</td> <td> 0.000</td> <td>   27.026</td> <td>   29.123</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[6]</th>   <td>   31.2898</td> <td>    0.532</td> <td>   58.790</td> <td> 0.000</td> <td>   30.247</td> <td>   32.333</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[9]</th>   <td>   17.6154</td> <td>    0.530</td> <td>   33.238</td> <td> 0.000</td> <td>   16.577</td> <td>   18.654</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[10]</th>  <td>   21.6280</td> <td>    0.532</td> <td>   40.647</td> <td> 0.000</td> <td>   20.585</td> <td>   22.671</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[15]</th>  <td>   26.7685</td> <td>    0.531</td> <td>   50.403</td> <td> 0.000</td> <td>   25.728</td> <td>   27.809</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[17]</th>  <td>   22.1958</td> <td>    0.534</td> <td>   41.603</td> <td> 0.000</td> <td>   21.150</td> <td>   23.242</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[19]</th>  <td>   21.0727</td> <td>    0.534</td> <td>   39.439</td> <td> 0.000</td> <td>   20.025</td> <td>   22.120</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[31]</th>  <td>   17.2141</td> <td>    0.533</td> <td>   32.286</td> <td> 0.000</td> <td>   16.169</td> <td>   18.259</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[32]</th>  <td>   30.9114</td> <td>    0.531</td> <td>   58.204</td> <td> 0.000</td> <td>   29.870</td> <td>   31.952</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[33]</th>  <td>   23.0237</td> <td>    0.533</td> <td>   43.229</td> <td> 0.000</td> <td>   21.980</td> <td>   24.068</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[36]</th>  <td>   22.9904</td> <td>    0.534</td> <td>   43.090</td> <td> 0.000</td> <td>   21.945</td> <td>   24.036</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[39]</th>  <td>   21.3079</td> <td>    0.533</td> <td>   40.008</td> <td> 0.000</td> <td>   20.264</td> <td>   22.352</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[41]</th>  <td>   18.7402</td> <td>    0.533</td> <td>   35.172</td> <td> 0.000</td> <td>   17.696</td> <td>   19.784</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[45]</th>  <td>   59.8057</td> <td>    0.539</td> <td>  110.972</td> <td> 0.000</td> <td>   58.749</td> <td>   60.862</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[47]</th>  <td>   34.0718</td> <td>    0.527</td> <td>   64.631</td> <td> 0.000</td> <td>   33.039</td> <td>   35.105</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[49]</th>  <td>   30.4449</td> <td>    0.531</td> <td>   57.316</td> <td> 0.000</td> <td>   29.404</td> <td>   31.486</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[50]</th>  <td>   32.5986</td> <td>    0.532</td> <td>   61.222</td> <td> 0.000</td> <td>   31.555</td> <td>   33.642</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[54]</th>  <td>   27.1295</td> <td>    0.534</td> <td>   50.824</td> <td> 0.000</td> <td>   26.083</td> <td>   28.176</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[56]</th>  <td>   24.0391</td> <td>    0.533</td> <td>   45.139</td> <td> 0.000</td> <td>   22.995</td> <td>   25.083</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[57]</th>  <td>   31.1271</td> <td>    0.532</td> <td>   58.548</td> <td> 0.000</td> <td>   30.085</td> <td>   32.169</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[61]</th>  <td>   22.2038</td> <td>    0.520</td> <td>   42.705</td> <td> 0.000</td> <td>   21.185</td> <td>   23.223</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[64]</th>  <td>   32.6426</td> <td>    0.532</td> <td>   61.311</td> <td> 0.000</td> <td>   31.599</td> <td>   33.686</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[69]</th>  <td>   33.0875</td> <td>    0.528</td> <td>   62.626</td> <td> 0.000</td> <td>   32.052</td> <td>   34.123</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[70]</th>  <td>   23.6832</td> <td>    0.540</td> <td>   43.894</td> <td> 0.000</td> <td>   22.626</td> <td>   24.741</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[72]</th>  <td>   34.1901</td> <td>    0.532</td> <td>   64.249</td> <td> 0.000</td> <td>   33.147</td> <td>   35.233</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[75]</th>  <td>   39.7373</td> <td>    0.533</td> <td>   74.522</td> <td> 0.000</td> <td>   38.692</td> <td>   40.782</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[76]</th>  <td>   62.0598</td> <td>    0.535</td> <td>  116.063</td> <td> 0.000</td> <td>   61.012</td> <td>   63.108</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[77]</th>  <td>   30.9785</td> <td>    0.534</td> <td>   58.058</td> <td> 0.000</td> <td>   29.933</td> <td>   32.024</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[78]</th>  <td>   27.1171</td> <td>    0.536</td> <td>   50.635</td> <td> 0.000</td> <td>   26.067</td> <td>   28.167</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[90]</th>  <td>   14.6493</td> <td>    0.530</td> <td>   27.641</td> <td> 0.000</td> <td>   13.611</td> <td>   15.688</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[96]</th>  <td>   40.1145</td> <td>    0.532</td> <td>   75.362</td> <td> 0.000</td> <td>   39.071</td> <td>   41.158</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[97]</th>  <td>   13.8750</td> <td>    0.534</td> <td>   25.998</td> <td> 0.000</td> <td>   12.829</td> <td>   14.921</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[101]</th> <td>   43.9873</td> <td>    0.530</td> <td>   82.993</td> <td> 0.000</td> <td>   42.949</td> <td>   45.026</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[102]</th> <td>   34.0557</td> <td>    0.532</td> <td>   64.029</td> <td> 0.000</td> <td>   33.013</td> <td>   35.098</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_1</th>            <td>   -2.1295</td> <td>    2.202</td> <td>   -0.967</td> <td> 0.333</td> <td>   -6.445</td> <td>    2.186</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_2</th>            <td>   -2.1209</td> <td>    2.391</td> <td>   -0.887</td> <td> 0.375</td> <td>   -6.807</td> <td>    2.565</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_3</th>            <td>   -1.3149</td> <td>    2.261</td> <td>   -0.582</td> <td> 0.561</td> <td>   -5.746</td> <td>    3.116</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_4</th>            <td>    0.2591</td> <td>    1.375</td> <td>    0.188</td> <td> 0.851</td> <td>   -2.436</td> <td>    2.954</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_5</th>            <td>    0.5321</td> <td>    0.690</td> <td>    0.771</td> <td> 0.441</td> <td>   -0.820</td> <td>    1.884</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_7</th>            <td>   -1.4152</td> <td>    2.347</td> <td>   -0.603</td> <td> 0.547</td> <td>   -6.016</td> <td>    3.186</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_8</th>            <td>    5.7250</td> <td>    1.531</td> <td>    3.739</td> <td> 0.000</td> <td>    2.724</td> <td>    8.726</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_9</th>            <td>    3.0175</td> <td>    1.263</td> <td>    2.388</td> <td> 0.017</td> <td>    0.541</td> <td>    5.494</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_10</th>           <td>    2.9866</td> <td>    1.244</td> <td>    2.402</td> <td> 0.016</td> <td>    0.549</td> <td>    5.424</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_11</th>           <td>    4.0696</td> <td>    2.076</td> <td>    1.960</td> <td> 0.050</td> <td>-1.94e-05</td> <td>    8.139</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_12</th>           <td>    0.5207</td> <td>    0.881</td> <td>    0.591</td> <td> 0.554</td> <td>   -1.205</td> <td>    2.247</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_13</th>           <td>    1.5954</td> <td>    0.660</td> <td>    2.418</td> <td> 0.016</td> <td>    0.302</td> <td>    2.889</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_14</th>           <td>    2.0711</td> <td>    0.887</td> <td>    2.335</td> <td> 0.020</td> <td>    0.333</td> <td>    3.809</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_15</th>           <td>    2.3969</td> <td>    1.463</td> <td>    1.638</td> <td> 0.101</td> <td>   -0.471</td> <td>    5.265</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_16</th>           <td>    3.1541</td> <td>    1.607</td> <td>    1.963</td> <td> 0.050</td> <td>    0.005</td> <td>    6.303</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_17</th>           <td>    3.1007</td> <td>    1.400</td> <td>    2.216</td> <td> 0.027</td> <td>    0.358</td> <td>    5.844</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_18</th>           <td>    2.5068</td> <td>    0.785</td> <td>    3.194</td> <td> 0.001</td> <td>    0.968</td> <td>    4.045</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_19</th>           <td>    1.2068</td> <td>    0.876</td> <td>    1.378</td> <td> 0.168</td> <td>   -0.510</td> <td>    2.923</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_20</th>           <td>    2.5219</td> <td>    1.174</td> <td>    2.147</td> <td> 0.032</td> <td>    0.220</td> <td>    4.824</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_21</th>           <td>    3.9398</td> <td>    1.348</td> <td>    2.924</td> <td> 0.003</td> <td>    1.299</td> <td>    6.581</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_22</th>           <td>    3.0415</td> <td>    0.978</td> <td>    3.110</td> <td> 0.002</td> <td>    1.125</td> <td>    4.958</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_23</th>           <td>    3.1701</td> <td>    1.038</td> <td>    3.053</td> <td> 0.002</td> <td>    1.135</td> <td>    5.205</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_24</th>           <td>    2.9900</td> <td>    0.981</td> <td>    3.048</td> <td> 0.002</td> <td>    1.067</td> <td>    4.913</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_25</th>           <td>    2.9883</td> <td>    0.865</td> <td>    3.455</td> <td> 0.001</td> <td>    1.293</td> <td>    4.683</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_26</th>           <td>    1.6617</td> <td>    0.697</td> <td>    2.382</td> <td> 0.017</td> <td>    0.295</td> <td>    3.029</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_27</th>           <td>    2.6439</td> <td>    1.196</td> <td>    2.210</td> <td> 0.027</td> <td>    0.299</td> <td>    4.988</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_28</th>           <td>    3.3798</td> <td>    1.115</td> <td>    3.030</td> <td> 0.002</td> <td>    1.194</td> <td>    5.566</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_29</th>           <td>    3.0522</td> <td>    0.939</td> <td>    3.252</td> <td> 0.001</td> <td>    1.213</td> <td>    4.892</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>5277950.741</td> <th>  Durbin-Watson:     </th>    <td>   1.984</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>   <td> 0.000</td>    <th>  Jarque-Bera (JB):  </th> <td>23990524403.819</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>            <td>15.870</td>    <th>  Prob(JB):          </th>    <td>    0.00</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>        <td>466.662</td>   <th>  Cond. No.          </th>    <td>    18.0</td>    \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors are robust to cluster correlation (cluster)"
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &     mobility     & \\textbf{  R-squared:         } &        0.025     \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &        0.025     \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &          nan     \\\\\n",
       "\\textbf{Date:}             & Wed, 21 May 2025 & \\textbf{  Prob (F-statistic):} &         nan      \\\\\n",
       "\\textbf{Time:}             &     11:31:37     & \\textbf{  Log-Likelihood:    } &   -1.5556e+07    \\\\\n",
       "\\textbf{No. Observations:} &     2665737      & \\textbf{  AIC:               } &    3.111e+07     \\\\\n",
       "\\textbf{Df Residuals:}     &     2665673      & \\textbf{  BIC:               } &    3.111e+07     \\\\\n",
       "\\textbf{Df Model:}         &          63      & \\textbf{                     } &                  \\\\\n",
       "\\textbf{Covariance Type:}  &     cluster      & \\textbf{                     } &                  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                        & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{C(strike)[3]}   &      23.5447  &        0.537     &    43.806  &         0.000        &       22.491    &       24.598     \\\\\n",
       "\\textbf{C(strike)[4]}   &      28.9998  &        0.530     &    54.747  &         0.000        &       27.962    &       30.038     \\\\\n",
       "\\textbf{C(strike)[5]}   &      28.0745  &        0.535     &    52.461  &         0.000        &       27.026    &       29.123     \\\\\n",
       "\\textbf{C(strike)[6]}   &      31.2898  &        0.532     &    58.790  &         0.000        &       30.247    &       32.333     \\\\\n",
       "\\textbf{C(strike)[9]}   &      17.6154  &        0.530     &    33.238  &         0.000        &       16.577    &       18.654     \\\\\n",
       "\\textbf{C(strike)[10]}  &      21.6280  &        0.532     &    40.647  &         0.000        &       20.585    &       22.671     \\\\\n",
       "\\textbf{C(strike)[15]}  &      26.7685  &        0.531     &    50.403  &         0.000        &       25.728    &       27.809     \\\\\n",
       "\\textbf{C(strike)[17]}  &      22.1958  &        0.534     &    41.603  &         0.000        &       21.150    &       23.242     \\\\\n",
       "\\textbf{C(strike)[19]}  &      21.0727  &        0.534     &    39.439  &         0.000        &       20.025    &       22.120     \\\\\n",
       "\\textbf{C(strike)[31]}  &      17.2141  &        0.533     &    32.286  &         0.000        &       16.169    &       18.259     \\\\\n",
       "\\textbf{C(strike)[32]}  &      30.9114  &        0.531     &    58.204  &         0.000        &       29.870    &       31.952     \\\\\n",
       "\\textbf{C(strike)[33]}  &      23.0237  &        0.533     &    43.229  &         0.000        &       21.980    &       24.068     \\\\\n",
       "\\textbf{C(strike)[36]}  &      22.9904  &        0.534     &    43.090  &         0.000        &       21.945    &       24.036     \\\\\n",
       "\\textbf{C(strike)[39]}  &      21.3079  &        0.533     &    40.008  &         0.000        &       20.264    &       22.352     \\\\\n",
       "\\textbf{C(strike)[41]}  &      18.7402  &        0.533     &    35.172  &         0.000        &       17.696    &       19.784     \\\\\n",
       "\\textbf{C(strike)[45]}  &      59.8057  &        0.539     &   110.972  &         0.000        &       58.749    &       60.862     \\\\\n",
       "\\textbf{C(strike)[47]}  &      34.0718  &        0.527     &    64.631  &         0.000        &       33.039    &       35.105     \\\\\n",
       "\\textbf{C(strike)[49]}  &      30.4449  &        0.531     &    57.316  &         0.000        &       29.404    &       31.486     \\\\\n",
       "\\textbf{C(strike)[50]}  &      32.5986  &        0.532     &    61.222  &         0.000        &       31.555    &       33.642     \\\\\n",
       "\\textbf{C(strike)[54]}  &      27.1295  &        0.534     &    50.824  &         0.000        &       26.083    &       28.176     \\\\\n",
       "\\textbf{C(strike)[56]}  &      24.0391  &        0.533     &    45.139  &         0.000        &       22.995    &       25.083     \\\\\n",
       "\\textbf{C(strike)[57]}  &      31.1271  &        0.532     &    58.548  &         0.000        &       30.085    &       32.169     \\\\\n",
       "\\textbf{C(strike)[61]}  &      22.2038  &        0.520     &    42.705  &         0.000        &       21.185    &       23.223     \\\\\n",
       "\\textbf{C(strike)[64]}  &      32.6426  &        0.532     &    61.311  &         0.000        &       31.599    &       33.686     \\\\\n",
       "\\textbf{C(strike)[69]}  &      33.0875  &        0.528     &    62.626  &         0.000        &       32.052    &       34.123     \\\\\n",
       "\\textbf{C(strike)[70]}  &      23.6832  &        0.540     &    43.894  &         0.000        &       22.626    &       24.741     \\\\\n",
       "\\textbf{C(strike)[72]}  &      34.1901  &        0.532     &    64.249  &         0.000        &       33.147    &       35.233     \\\\\n",
       "\\textbf{C(strike)[75]}  &      39.7373  &        0.533     &    74.522  &         0.000        &       38.692    &       40.782     \\\\\n",
       "\\textbf{C(strike)[76]}  &      62.0598  &        0.535     &   116.063  &         0.000        &       61.012    &       63.108     \\\\\n",
       "\\textbf{C(strike)[77]}  &      30.9785  &        0.534     &    58.058  &         0.000        &       29.933    &       32.024     \\\\\n",
       "\\textbf{C(strike)[78]}  &      27.1171  &        0.536     &    50.635  &         0.000        &       26.067    &       28.167     \\\\\n",
       "\\textbf{C(strike)[90]}  &      14.6493  &        0.530     &    27.641  &         0.000        &       13.611    &       15.688     \\\\\n",
       "\\textbf{C(strike)[96]}  &      40.1145  &        0.532     &    75.362  &         0.000        &       39.071    &       41.158     \\\\\n",
       "\\textbf{C(strike)[97]}  &      13.8750  &        0.534     &    25.998  &         0.000        &       12.829    &       14.921     \\\\\n",
       "\\textbf{C(strike)[101]} &      43.9873  &        0.530     &    82.993  &         0.000        &       42.949    &       45.026     \\\\\n",
       "\\textbf{C(strike)[102]} &      34.0557  &        0.532     &    64.029  &         0.000        &       33.013    &       35.098     \\\\\n",
       "\\textbf{X\\_1}           &      -2.1295  &        2.202     &    -0.967  &         0.333        &       -6.445    &        2.186     \\\\\n",
       "\\textbf{X\\_2}           &      -2.1209  &        2.391     &    -0.887  &         0.375        &       -6.807    &        2.565     \\\\\n",
       "\\textbf{X\\_3}           &      -1.3149  &        2.261     &    -0.582  &         0.561        &       -5.746    &        3.116     \\\\\n",
       "\\textbf{X\\_4}           &       0.2591  &        1.375     &     0.188  &         0.851        &       -2.436    &        2.954     \\\\\n",
       "\\textbf{X\\_5}           &       0.5321  &        0.690     &     0.771  &         0.441        &       -0.820    &        1.884     \\\\\n",
       "\\textbf{X\\_7}           &      -1.4152  &        2.347     &    -0.603  &         0.547        &       -6.016    &        3.186     \\\\\n",
       "\\textbf{X\\_8}           &       5.7250  &        1.531     &     3.739  &         0.000        &        2.724    &        8.726     \\\\\n",
       "\\textbf{X\\_9}           &       3.0175  &        1.263     &     2.388  &         0.017        &        0.541    &        5.494     \\\\\n",
       "\\textbf{X\\_10}          &       2.9866  &        1.244     &     2.402  &         0.016        &        0.549    &        5.424     \\\\\n",
       "\\textbf{X\\_11}          &       4.0696  &        2.076     &     1.960  &         0.050        &    -1.94e-05    &        8.139     \\\\\n",
       "\\textbf{X\\_12}          &       0.5207  &        0.881     &     0.591  &         0.554        &       -1.205    &        2.247     \\\\\n",
       "\\textbf{X\\_13}          &       1.5954  &        0.660     &     2.418  &         0.016        &        0.302    &        2.889     \\\\\n",
       "\\textbf{X\\_14}          &       2.0711  &        0.887     &     2.335  &         0.020        &        0.333    &        3.809     \\\\\n",
       "\\textbf{X\\_15}          &       2.3969  &        1.463     &     1.638  &         0.101        &       -0.471    &        5.265     \\\\\n",
       "\\textbf{X\\_16}          &       3.1541  &        1.607     &     1.963  &         0.050        &        0.005    &        6.303     \\\\\n",
       "\\textbf{X\\_17}          &       3.1007  &        1.400     &     2.216  &         0.027        &        0.358    &        5.844     \\\\\n",
       "\\textbf{X\\_18}          &       2.5068  &        0.785     &     3.194  &         0.001        &        0.968    &        4.045     \\\\\n",
       "\\textbf{X\\_19}          &       1.2068  &        0.876     &     1.378  &         0.168        &       -0.510    &        2.923     \\\\\n",
       "\\textbf{X\\_20}          &       2.5219  &        1.174     &     2.147  &         0.032        &        0.220    &        4.824     \\\\\n",
       "\\textbf{X\\_21}          &       3.9398  &        1.348     &     2.924  &         0.003        &        1.299    &        6.581     \\\\\n",
       "\\textbf{X\\_22}          &       3.0415  &        0.978     &     3.110  &         0.002        &        1.125    &        4.958     \\\\\n",
       "\\textbf{X\\_23}          &       3.1701  &        1.038     &     3.053  &         0.002        &        1.135    &        5.205     \\\\\n",
       "\\textbf{X\\_24}          &       2.9900  &        0.981     &     3.048  &         0.002        &        1.067    &        4.913     \\\\\n",
       "\\textbf{X\\_25}          &       2.9883  &        0.865     &     3.455  &         0.001        &        1.293    &        4.683     \\\\\n",
       "\\textbf{X\\_26}          &       1.6617  &        0.697     &     2.382  &         0.017        &        0.295    &        3.029     \\\\\n",
       "\\textbf{X\\_27}          &       2.6439  &        1.196     &     2.210  &         0.027        &        0.299    &        4.988     \\\\\n",
       "\\textbf{X\\_28}          &       3.3798  &        1.115     &     3.030  &         0.002        &        1.194    &        5.566     \\\\\n",
       "\\textbf{X\\_29}          &       3.0522  &        0.939     &     3.252  &         0.001        &        1.213    &        4.892     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 5277950.741 & \\textbf{  Durbin-Watson:     } &        1.984     \\\\\n",
       "\\textbf{Prob(Omnibus):} &     0.000   & \\textbf{  Jarque-Bera (JB):  } & 23990524403.819  \\\\\n",
       "\\textbf{Skew:}          &    15.870   & \\textbf{  Prob(JB):          } &         0.00     \\\\\n",
       "\\textbf{Kurtosis:}      &   466.662   & \\textbf{  Cond. No.          } &         18.0     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors are robust to cluster correlation (cluster)"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:               mobility   R-squared:                       0.025\n",
       "Model:                            OLS   Adj. R-squared:                  0.025\n",
       "Method:                 Least Squares   F-statistic:                       nan\n",
       "Date:                Wed, 21 May 2025   Prob (F-statistic):                nan\n",
       "Time:                        11:31:37   Log-Likelihood:            -1.5556e+07\n",
       "No. Observations:             2665737   AIC:                         3.111e+07\n",
       "Df Residuals:                 2665673   BIC:                         3.111e+07\n",
       "Df Model:                          63                                         \n",
       "Covariance Type:              cluster                                         \n",
       "==================================================================================\n",
       "                     coef    std err          z      P>|z|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------\n",
       "C(strike)[3]      23.5447      0.537     43.806      0.000      22.491      24.598\n",
       "C(strike)[4]      28.9998      0.530     54.747      0.000      27.962      30.038\n",
       "C(strike)[5]      28.0745      0.535     52.461      0.000      27.026      29.123\n",
       "C(strike)[6]      31.2898      0.532     58.790      0.000      30.247      32.333\n",
       "C(strike)[9]      17.6154      0.530     33.238      0.000      16.577      18.654\n",
       "C(strike)[10]     21.6280      0.532     40.647      0.000      20.585      22.671\n",
       "C(strike)[15]     26.7685      0.531     50.403      0.000      25.728      27.809\n",
       "C(strike)[17]     22.1958      0.534     41.603      0.000      21.150      23.242\n",
       "C(strike)[19]     21.0727      0.534     39.439      0.000      20.025      22.120\n",
       "C(strike)[31]     17.2141      0.533     32.286      0.000      16.169      18.259\n",
       "C(strike)[32]     30.9114      0.531     58.204      0.000      29.870      31.952\n",
       "C(strike)[33]     23.0237      0.533     43.229      0.000      21.980      24.068\n",
       "C(strike)[36]     22.9904      0.534     43.090      0.000      21.945      24.036\n",
       "C(strike)[39]     21.3079      0.533     40.008      0.000      20.264      22.352\n",
       "C(strike)[41]     18.7402      0.533     35.172      0.000      17.696      19.784\n",
       "C(strike)[45]     59.8057      0.539    110.972      0.000      58.749      60.862\n",
       "C(strike)[47]     34.0718      0.527     64.631      0.000      33.039      35.105\n",
       "C(strike)[49]     30.4449      0.531     57.316      0.000      29.404      31.486\n",
       "C(strike)[50]     32.5986      0.532     61.222      0.000      31.555      33.642\n",
       "C(strike)[54]     27.1295      0.534     50.824      0.000      26.083      28.176\n",
       "C(strike)[56]     24.0391      0.533     45.139      0.000      22.995      25.083\n",
       "C(strike)[57]     31.1271      0.532     58.548      0.000      30.085      32.169\n",
       "C(strike)[61]     22.2038      0.520     42.705      0.000      21.185      23.223\n",
       "C(strike)[64]     32.6426      0.532     61.311      0.000      31.599      33.686\n",
       "C(strike)[69]     33.0875      0.528     62.626      0.000      32.052      34.123\n",
       "C(strike)[70]     23.6832      0.540     43.894      0.000      22.626      24.741\n",
       "C(strike)[72]     34.1901      0.532     64.249      0.000      33.147      35.233\n",
       "C(strike)[75]     39.7373      0.533     74.522      0.000      38.692      40.782\n",
       "C(strike)[76]     62.0598      0.535    116.063      0.000      61.012      63.108\n",
       "C(strike)[77]     30.9785      0.534     58.058      0.000      29.933      32.024\n",
       "C(strike)[78]     27.1171      0.536     50.635      0.000      26.067      28.167\n",
       "C(strike)[90]     14.6493      0.530     27.641      0.000      13.611      15.688\n",
       "C(strike)[96]     40.1145      0.532     75.362      0.000      39.071      41.158\n",
       "C(strike)[97]     13.8750      0.534     25.998      0.000      12.829      14.921\n",
       "C(strike)[101]    43.9873      0.530     82.993      0.000      42.949      45.026\n",
       "C(strike)[102]    34.0557      0.532     64.029      0.000      33.013      35.098\n",
       "X_1               -2.1295      2.202     -0.967      0.333      -6.445       2.186\n",
       "X_2               -2.1209      2.391     -0.887      0.375      -6.807       2.565\n",
       "X_3               -1.3149      2.261     -0.582      0.561      -5.746       3.116\n",
       "X_4                0.2591      1.375      0.188      0.851      -2.436       2.954\n",
       "X_5                0.5321      0.690      0.771      0.441      -0.820       1.884\n",
       "X_7               -1.4152      2.347     -0.603      0.547      -6.016       3.186\n",
       "X_8                5.7250      1.531      3.739      0.000       2.724       8.726\n",
       "X_9                3.0175      1.263      2.388      0.017       0.541       5.494\n",
       "X_10               2.9866      1.244      2.402      0.016       0.549       5.424\n",
       "X_11               4.0696      2.076      1.960      0.050   -1.94e-05       8.139\n",
       "X_12               0.5207      0.881      0.591      0.554      -1.205       2.247\n",
       "X_13               1.5954      0.660      2.418      0.016       0.302       2.889\n",
       "X_14               2.0711      0.887      2.335      0.020       0.333       3.809\n",
       "X_15               2.3969      1.463      1.638      0.101      -0.471       5.265\n",
       "X_16               3.1541      1.607      1.963      0.050       0.005       6.303\n",
       "X_17               3.1007      1.400      2.216      0.027       0.358       5.844\n",
       "X_18               2.5068      0.785      3.194      0.001       0.968       4.045\n",
       "X_19               1.2068      0.876      1.378      0.168      -0.510       2.923\n",
       "X_20               2.5219      1.174      2.147      0.032       0.220       4.824\n",
       "X_21               3.9398      1.348      2.924      0.003       1.299       6.581\n",
       "X_22               3.0415      0.978      3.110      0.002       1.125       4.958\n",
       "X_23               3.1701      1.038      3.053      0.002       1.135       5.205\n",
       "X_24               2.9900      0.981      3.048      0.002       1.067       4.913\n",
       "X_25               2.9883      0.865      3.455      0.001       1.293       4.683\n",
       "X_26               1.6617      0.697      2.382      0.017       0.295       3.029\n",
       "X_27               2.6439      1.196      2.210      0.027       0.299       4.988\n",
       "X_28               3.3798      1.115      3.030      0.002       1.194       5.566\n",
       "X_29               3.0522      0.939      3.252      0.001       1.213       4.892\n",
       "==============================================================================\n",
       "Omnibus:                  5277950.741   Durbin-Watson:                   1.984\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):      23990524403.819\n",
       "Skew:                          15.870   Prob(JB):                         0.00\n",
       "Kurtosis:                     466.662   Cond. No.                         18.0\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors are robust to cluster correlation (cluster)\n",
       "\"\"\""
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# run the event study regression for mobility around strikes\n",
    "# 7 lags and 21 leads\n",
    "# time vars provide the parameters of interest\n",
    "# drop the day = -2 variable for difference in means and to avoid multicollinearity\n",
    "# use strike fixed effects and cluster SE by strike\n",
    "\n",
    "reg = sm.ols('mobility ~ ' + var_form + ' +  C(strike) - 1', \n",
    "             data=df_high).fit(cov_type='cluster',\n",
    "                          cov_kwds={'groups': df_high['strike'].values})\n",
    "\n",
    "# save parameters and confidence intervals for plotting\n",
    "conf_int = reg.conf_int()\n",
    "res_density_high = pd.DataFrame({\n",
    "    'params': reg.params,\n",
    "    'ci_l': conf_int[0],  # lower bound\n",
    "    'ci_h': conf_int[1],  # upper bound\n",
    "})\n",
    "\n",
    "# print regression summary\n",
    "reg.summary()\n",
    "# reindex the results to go from day = -7 to day = 21\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>mobility</td>     <th>  R-squared:         </th>  <td>   0.011</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.010</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>     nan</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 21 May 2025</td> <th>  Prob (F-statistic):</th>   <td>   nan</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>11:31:52</td>     <th>  Log-Likelihood:    </th> <td>-3.9202e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>714897</td>      <th>  AIC:               </th>  <td>7.840e+06</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>714831</td>      <th>  BIC:               </th>  <td>7.841e+06</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    65</td>      <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>       <td>cluster</td>     <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>           <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[1]</th>   <td>   23.2702</td> <td>    0.807</td> <td>   28.834</td> <td> 0.000</td> <td>   21.688</td> <td>   24.852</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[7]</th>   <td>   27.3933</td> <td>    0.814</td> <td>   33.668</td> <td> 0.000</td> <td>   25.799</td> <td>   28.988</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[8]</th>   <td>   26.9923</td> <td>    0.815</td> <td>   33.121</td> <td> 0.000</td> <td>   25.395</td> <td>   28.590</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[11]</th>  <td>   31.2638</td> <td>    0.815</td> <td>   38.366</td> <td> 0.000</td> <td>   29.667</td> <td>   32.861</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[12]</th>  <td>   21.9379</td> <td>    0.805</td> <td>   27.267</td> <td> 0.000</td> <td>   20.361</td> <td>   23.515</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[14]</th>  <td>   16.0922</td> <td>    0.816</td> <td>   19.731</td> <td> 0.000</td> <td>   14.494</td> <td>   17.691</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[20]</th>  <td>   22.8337</td> <td>    0.812</td> <td>   28.111</td> <td> 0.000</td> <td>   21.242</td> <td>   24.426</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[24]</th>  <td>   40.4166</td> <td>    0.819</td> <td>   49.341</td> <td> 0.000</td> <td>   38.811</td> <td>   42.022</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[26]</th>  <td>   23.8187</td> <td>    0.785</td> <td>   30.323</td> <td> 0.000</td> <td>   22.279</td> <td>   25.358</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[27]</th>  <td>   22.2907</td> <td>    0.816</td> <td>   27.328</td> <td> 0.000</td> <td>   20.692</td> <td>   23.889</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[28]</th>  <td>   23.4145</td> <td>    0.816</td> <td>   28.681</td> <td> 0.000</td> <td>   21.814</td> <td>   25.015</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[34]</th>  <td>   16.7720</td> <td>    0.812</td> <td>   20.653</td> <td> 0.000</td> <td>   15.180</td> <td>   18.364</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[35]</th>  <td>   16.7807</td> <td>    0.815</td> <td>   20.596</td> <td> 0.000</td> <td>   15.184</td> <td>   18.378</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[37]</th>  <td>   18.8843</td> <td>    0.816</td> <td>   23.130</td> <td> 0.000</td> <td>   17.284</td> <td>   20.485</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[38]</th>  <td>   15.9024</td> <td>    0.812</td> <td>   19.584</td> <td> 0.000</td> <td>   14.311</td> <td>   17.494</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[40]</th>  <td>   19.6602</td> <td>    0.814</td> <td>   24.161</td> <td> 0.000</td> <td>   18.065</td> <td>   21.255</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[42]</th>  <td>   18.0329</td> <td>    0.813</td> <td>   22.192</td> <td> 0.000</td> <td>   16.440</td> <td>   19.626</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[43]</th>  <td>   25.4588</td> <td>    0.816</td> <td>   31.201</td> <td> 0.000</td> <td>   23.860</td> <td>   27.058</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[48]</th>  <td>   19.5715</td> <td>    0.811</td> <td>   24.136</td> <td> 0.000</td> <td>   17.982</td> <td>   21.161</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[51]</th>  <td>   43.2925</td> <td>    0.814</td> <td>   53.160</td> <td> 0.000</td> <td>   41.696</td> <td>   44.889</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[58]</th>  <td>   26.8376</td> <td>    0.811</td> <td>   33.110</td> <td> 0.000</td> <td>   25.249</td> <td>   28.426</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[59]</th>  <td>   18.9336</td> <td>    0.815</td> <td>   23.238</td> <td> 0.000</td> <td>   17.337</td> <td>   20.531</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[60]</th>  <td>   29.2169</td> <td>    0.805</td> <td>   36.305</td> <td> 0.000</td> <td>   27.640</td> <td>   30.794</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[62]</th>  <td>   27.8951</td> <td>    0.812</td> <td>   34.351</td> <td> 0.000</td> <td>   26.304</td> <td>   29.487</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[65]</th>  <td>   41.5709</td> <td>    0.810</td> <td>   51.296</td> <td> 0.000</td> <td>   39.982</td> <td>   43.159</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[67]</th>  <td>   30.2462</td> <td>    0.803</td> <td>   37.645</td> <td> 0.000</td> <td>   28.672</td> <td>   31.821</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[68]</th>  <td>   28.4391</td> <td>    0.802</td> <td>   35.460</td> <td> 0.000</td> <td>   26.867</td> <td>   30.011</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[71]</th>  <td>   32.9522</td> <td>    0.812</td> <td>   40.586</td> <td> 0.000</td> <td>   31.361</td> <td>   34.544</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[81]</th>  <td>   39.9433</td> <td>    0.810</td> <td>   49.335</td> <td> 0.000</td> <td>   38.356</td> <td>   41.530</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[82]</th>  <td>   27.8771</td> <td>    0.813</td> <td>   34.281</td> <td> 0.000</td> <td>   26.283</td> <td>   29.471</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[83]</th>  <td>   28.2981</td> <td>    0.812</td> <td>   34.869</td> <td> 0.000</td> <td>   26.707</td> <td>   29.889</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[85]</th>  <td>   26.9506</td> <td>    0.811</td> <td>   33.248</td> <td> 0.000</td> <td>   25.362</td> <td>   28.539</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[87]</th>  <td>   20.7616</td> <td>    0.814</td> <td>   25.499</td> <td> 0.000</td> <td>   19.166</td> <td>   22.357</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[92]</th>  <td>   16.2769</td> <td>    0.813</td> <td>   20.015</td> <td> 0.000</td> <td>   14.683</td> <td>   17.871</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[95]</th>  <td>   19.9800</td> <td>    0.817</td> <td>   24.469</td> <td> 0.000</td> <td>   18.380</td> <td>   21.580</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[100]</th> <td>   29.9857</td> <td>    0.813</td> <td>   36.875</td> <td> 0.000</td> <td>   28.392</td> <td>   31.579</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[106]</th> <td>   23.8239</td> <td>    0.748</td> <td>   31.863</td> <td> 0.000</td> <td>   22.358</td> <td>   25.289</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[107]</th> <td>   31.7072</td> <td>    0.701</td> <td>   45.248</td> <td> 0.000</td> <td>   30.334</td> <td>   33.081</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_1</th>            <td>    1.0739</td> <td>    1.388</td> <td>    0.774</td> <td> 0.439</td> <td>   -1.647</td> <td>    3.794</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_2</th>            <td>    1.4137</td> <td>    1.403</td> <td>    1.008</td> <td> 0.314</td> <td>   -1.335</td> <td>    4.163</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_3</th>            <td>    0.2125</td> <td>    1.149</td> <td>    0.185</td> <td> 0.853</td> <td>   -2.040</td> <td>    2.464</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_4</th>            <td>   -0.0388</td> <td>    0.869</td> <td>   -0.045</td> <td> 0.964</td> <td>   -1.742</td> <td>    1.665</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_5</th>            <td>    0.8241</td> <td>    0.776</td> <td>    1.062</td> <td> 0.288</td> <td>   -0.697</td> <td>    2.346</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_7</th>            <td>    0.8414</td> <td>    1.036</td> <td>    0.812</td> <td> 0.417</td> <td>   -1.189</td> <td>    2.871</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_8</th>            <td>    9.1386</td> <td>    1.940</td> <td>    4.712</td> <td> 0.000</td> <td>    5.337</td> <td>   12.940</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_9</th>            <td>    4.9364</td> <td>    1.422</td> <td>    3.471</td> <td> 0.001</td> <td>    2.149</td> <td>    7.724</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_10</th>           <td>    2.4719</td> <td>    1.305</td> <td>    1.894</td> <td> 0.058</td> <td>   -0.085</td> <td>    5.029</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_11</th>           <td>    2.6319</td> <td>    1.420</td> <td>    1.853</td> <td> 0.064</td> <td>   -0.151</td> <td>    5.415</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_12</th>           <td>    0.9831</td> <td>    1.031</td> <td>    0.954</td> <td> 0.340</td> <td>   -1.037</td> <td>    3.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_13</th>           <td>    0.2582</td> <td>    0.860</td> <td>    0.300</td> <td> 0.764</td> <td>   -1.428</td> <td>    1.944</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_14</th>           <td>    1.5257</td> <td>    1.345</td> <td>    1.135</td> <td> 0.257</td> <td>   -1.110</td> <td>    4.161</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_15</th>           <td>    0.4466</td> <td>    1.044</td> <td>    0.428</td> <td> 0.669</td> <td>   -1.600</td> <td>    2.493</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_16</th>           <td>    1.2663</td> <td>    1.421</td> <td>    0.891</td> <td> 0.373</td> <td>   -1.519</td> <td>    4.052</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_17</th>           <td>    0.0291</td> <td>    1.177</td> <td>    0.025</td> <td> 0.980</td> <td>   -2.279</td> <td>    2.337</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_18</th>           <td>    0.5965</td> <td>    1.334</td> <td>    0.447</td> <td> 0.655</td> <td>   -2.017</td> <td>    3.210</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_19</th>           <td>    0.0385</td> <td>    1.562</td> <td>    0.025</td> <td> 0.980</td> <td>   -3.022</td> <td>    3.099</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_20</th>           <td>    0.3541</td> <td>    1.190</td> <td>    0.298</td> <td> 0.766</td> <td>   -1.978</td> <td>    2.687</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_21</th>           <td>    1.6308</td> <td>    1.446</td> <td>    1.128</td> <td> 0.259</td> <td>   -1.203</td> <td>    4.464</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_22</th>           <td>   -0.5183</td> <td>    1.454</td> <td>   -0.356</td> <td> 0.721</td> <td>   -3.368</td> <td>    2.331</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_23</th>           <td>    0.0004</td> <td>    1.281</td> <td>    0.000</td> <td> 1.000</td> <td>   -2.510</td> <td>    2.511</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_24</th>           <td>    0.4495</td> <td>    1.205</td> <td>    0.373</td> <td> 0.709</td> <td>   -1.913</td> <td>    2.812</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_25</th>           <td>    0.3535</td> <td>    1.166</td> <td>    0.303</td> <td> 0.762</td> <td>   -1.932</td> <td>    2.639</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_26</th>           <td>   -0.6282</td> <td>    1.212</td> <td>   -0.518</td> <td> 0.604</td> <td>   -3.003</td> <td>    1.747</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_27</th>           <td>   -1.9966</td> <td>    1.020</td> <td>   -1.958</td> <td> 0.050</td> <td>   -3.995</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_28</th>           <td>    1.2847</td> <td>    1.880</td> <td>    0.683</td> <td> 0.494</td> <td>   -2.401</td> <td>    4.970</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_29</th>           <td>    0.1943</td> <td>    0.943</td> <td>    0.206</td> <td> 0.837</td> <td>   -1.654</td> <td>    2.043</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>960400.477</td> <th>  Durbin-Watson:     </th>   <td>   1.983</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>   <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>626204035.997</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>            <td> 7.305</td>   <th>  Prob(JB):          </th>   <td>    0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>        <td>147.253</td>  <th>  Cond. No.          </th>   <td>    17.5</td>   \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors are robust to cluster correlation (cluster)"
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &     mobility     & \\textbf{  R-squared:         } &       0.011    \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &       0.010    \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &         nan    \\\\\n",
       "\\textbf{Date:}             & Wed, 21 May 2025 & \\textbf{  Prob (F-statistic):} &        nan     \\\\\n",
       "\\textbf{Time:}             &     11:31:52     & \\textbf{  Log-Likelihood:    } &  -3.9202e+06   \\\\\n",
       "\\textbf{No. Observations:} &      714897      & \\textbf{  AIC:               } &   7.840e+06    \\\\\n",
       "\\textbf{Df Residuals:}     &      714831      & \\textbf{  BIC:               } &   7.841e+06    \\\\\n",
       "\\textbf{Df Model:}         &          65      & \\textbf{                     } &                \\\\\n",
       "\\textbf{Covariance Type:}  &     cluster      & \\textbf{                     } &                \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                        & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{C(strike)[1]}   &      23.2702  &        0.807     &    28.834  &         0.000        &       21.688    &       24.852     \\\\\n",
       "\\textbf{C(strike)[7]}   &      27.3933  &        0.814     &    33.668  &         0.000        &       25.799    &       28.988     \\\\\n",
       "\\textbf{C(strike)[8]}   &      26.9923  &        0.815     &    33.121  &         0.000        &       25.395    &       28.590     \\\\\n",
       "\\textbf{C(strike)[11]}  &      31.2638  &        0.815     &    38.366  &         0.000        &       29.667    &       32.861     \\\\\n",
       "\\textbf{C(strike)[12]}  &      21.9379  &        0.805     &    27.267  &         0.000        &       20.361    &       23.515     \\\\\n",
       "\\textbf{C(strike)[14]}  &      16.0922  &        0.816     &    19.731  &         0.000        &       14.494    &       17.691     \\\\\n",
       "\\textbf{C(strike)[20]}  &      22.8337  &        0.812     &    28.111  &         0.000        &       21.242    &       24.426     \\\\\n",
       "\\textbf{C(strike)[24]}  &      40.4166  &        0.819     &    49.341  &         0.000        &       38.811    &       42.022     \\\\\n",
       "\\textbf{C(strike)[26]}  &      23.8187  &        0.785     &    30.323  &         0.000        &       22.279    &       25.358     \\\\\n",
       "\\textbf{C(strike)[27]}  &      22.2907  &        0.816     &    27.328  &         0.000        &       20.692    &       23.889     \\\\\n",
       "\\textbf{C(strike)[28]}  &      23.4145  &        0.816     &    28.681  &         0.000        &       21.814    &       25.015     \\\\\n",
       "\\textbf{C(strike)[34]}  &      16.7720  &        0.812     &    20.653  &         0.000        &       15.180    &       18.364     \\\\\n",
       "\\textbf{C(strike)[35]}  &      16.7807  &        0.815     &    20.596  &         0.000        &       15.184    &       18.378     \\\\\n",
       "\\textbf{C(strike)[37]}  &      18.8843  &        0.816     &    23.130  &         0.000        &       17.284    &       20.485     \\\\\n",
       "\\textbf{C(strike)[38]}  &      15.9024  &        0.812     &    19.584  &         0.000        &       14.311    &       17.494     \\\\\n",
       "\\textbf{C(strike)[40]}  &      19.6602  &        0.814     &    24.161  &         0.000        &       18.065    &       21.255     \\\\\n",
       "\\textbf{C(strike)[42]}  &      18.0329  &        0.813     &    22.192  &         0.000        &       16.440    &       19.626     \\\\\n",
       "\\textbf{C(strike)[43]}  &      25.4588  &        0.816     &    31.201  &         0.000        &       23.860    &       27.058     \\\\\n",
       "\\textbf{C(strike)[48]}  &      19.5715  &        0.811     &    24.136  &         0.000        &       17.982    &       21.161     \\\\\n",
       "\\textbf{C(strike)[51]}  &      43.2925  &        0.814     &    53.160  &         0.000        &       41.696    &       44.889     \\\\\n",
       "\\textbf{C(strike)[58]}  &      26.8376  &        0.811     &    33.110  &         0.000        &       25.249    &       28.426     \\\\\n",
       "\\textbf{C(strike)[59]}  &      18.9336  &        0.815     &    23.238  &         0.000        &       17.337    &       20.531     \\\\\n",
       "\\textbf{C(strike)[60]}  &      29.2169  &        0.805     &    36.305  &         0.000        &       27.640    &       30.794     \\\\\n",
       "\\textbf{C(strike)[62]}  &      27.8951  &        0.812     &    34.351  &         0.000        &       26.304    &       29.487     \\\\\n",
       "\\textbf{C(strike)[65]}  &      41.5709  &        0.810     &    51.296  &         0.000        &       39.982    &       43.159     \\\\\n",
       "\\textbf{C(strike)[67]}  &      30.2462  &        0.803     &    37.645  &         0.000        &       28.672    &       31.821     \\\\\n",
       "\\textbf{C(strike)[68]}  &      28.4391  &        0.802     &    35.460  &         0.000        &       26.867    &       30.011     \\\\\n",
       "\\textbf{C(strike)[71]}  &      32.9522  &        0.812     &    40.586  &         0.000        &       31.361    &       34.544     \\\\\n",
       "\\textbf{C(strike)[81]}  &      39.9433  &        0.810     &    49.335  &         0.000        &       38.356    &       41.530     \\\\\n",
       "\\textbf{C(strike)[82]}  &      27.8771  &        0.813     &    34.281  &         0.000        &       26.283    &       29.471     \\\\\n",
       "\\textbf{C(strike)[83]}  &      28.2981  &        0.812     &    34.869  &         0.000        &       26.707    &       29.889     \\\\\n",
       "\\textbf{C(strike)[85]}  &      26.9506  &        0.811     &    33.248  &         0.000        &       25.362    &       28.539     \\\\\n",
       "\\textbf{C(strike)[87]}  &      20.7616  &        0.814     &    25.499  &         0.000        &       19.166    &       22.357     \\\\\n",
       "\\textbf{C(strike)[92]}  &      16.2769  &        0.813     &    20.015  &         0.000        &       14.683    &       17.871     \\\\\n",
       "\\textbf{C(strike)[95]}  &      19.9800  &        0.817     &    24.469  &         0.000        &       18.380    &       21.580     \\\\\n",
       "\\textbf{C(strike)[100]} &      29.9857  &        0.813     &    36.875  &         0.000        &       28.392    &       31.579     \\\\\n",
       "\\textbf{C(strike)[106]} &      23.8239  &        0.748     &    31.863  &         0.000        &       22.358    &       25.289     \\\\\n",
       "\\textbf{C(strike)[107]} &      31.7072  &        0.701     &    45.248  &         0.000        &       30.334    &       33.081     \\\\\n",
       "\\textbf{X\\_1}           &       1.0739  &        1.388     &     0.774  &         0.439        &       -1.647    &        3.794     \\\\\n",
       "\\textbf{X\\_2}           &       1.4137  &        1.403     &     1.008  &         0.314        &       -1.335    &        4.163     \\\\\n",
       "\\textbf{X\\_3}           &       0.2125  &        1.149     &     0.185  &         0.853        &       -2.040    &        2.464     \\\\\n",
       "\\textbf{X\\_4}           &      -0.0388  &        0.869     &    -0.045  &         0.964        &       -1.742    &        1.665     \\\\\n",
       "\\textbf{X\\_5}           &       0.8241  &        0.776     &     1.062  &         0.288        &       -0.697    &        2.346     \\\\\n",
       "\\textbf{X\\_7}           &       0.8414  &        1.036     &     0.812  &         0.417        &       -1.189    &        2.871     \\\\\n",
       "\\textbf{X\\_8}           &       9.1386  &        1.940     &     4.712  &         0.000        &        5.337    &       12.940     \\\\\n",
       "\\textbf{X\\_9}           &       4.9364  &        1.422     &     3.471  &         0.001        &        2.149    &        7.724     \\\\\n",
       "\\textbf{X\\_10}          &       2.4719  &        1.305     &     1.894  &         0.058        &       -0.085    &        5.029     \\\\\n",
       "\\textbf{X\\_11}          &       2.6319  &        1.420     &     1.853  &         0.064        &       -0.151    &        5.415     \\\\\n",
       "\\textbf{X\\_12}          &       0.9831  &        1.031     &     0.954  &         0.340        &       -1.037    &        3.003     \\\\\n",
       "\\textbf{X\\_13}          &       0.2582  &        0.860     &     0.300  &         0.764        &       -1.428    &        1.944     \\\\\n",
       "\\textbf{X\\_14}          &       1.5257  &        1.345     &     1.135  &         0.257        &       -1.110    &        4.161     \\\\\n",
       "\\textbf{X\\_15}          &       0.4466  &        1.044     &     0.428  &         0.669        &       -1.600    &        2.493     \\\\\n",
       "\\textbf{X\\_16}          &       1.2663  &        1.421     &     0.891  &         0.373        &       -1.519    &        4.052     \\\\\n",
       "\\textbf{X\\_17}          &       0.0291  &        1.177     &     0.025  &         0.980        &       -2.279    &        2.337     \\\\\n",
       "\\textbf{X\\_18}          &       0.5965  &        1.334     &     0.447  &         0.655        &       -2.017    &        3.210     \\\\\n",
       "\\textbf{X\\_19}          &       0.0385  &        1.562     &     0.025  &         0.980        &       -3.022    &        3.099     \\\\\n",
       "\\textbf{X\\_20}          &       0.3541  &        1.190     &     0.298  &         0.766        &       -1.978    &        2.687     \\\\\n",
       "\\textbf{X\\_21}          &       1.6308  &        1.446     &     1.128  &         0.259        &       -1.203    &        4.464     \\\\\n",
       "\\textbf{X\\_22}          &      -0.5183  &        1.454     &    -0.356  &         0.721        &       -3.368    &        2.331     \\\\\n",
       "\\textbf{X\\_23}          &       0.0004  &        1.281     &     0.000  &         1.000        &       -2.510    &        2.511     \\\\\n",
       "\\textbf{X\\_24}          &       0.4495  &        1.205     &     0.373  &         0.709        &       -1.913    &        2.812     \\\\\n",
       "\\textbf{X\\_25}          &       0.3535  &        1.166     &     0.303  &         0.762        &       -1.932    &        2.639     \\\\\n",
       "\\textbf{X\\_26}          &      -0.6282  &        1.212     &    -0.518  &         0.604        &       -3.003    &        1.747     \\\\\n",
       "\\textbf{X\\_27}          &      -1.9966  &        1.020     &    -1.958  &         0.050        &       -3.995    &        0.002     \\\\\n",
       "\\textbf{X\\_28}          &       1.2847  &        1.880     &     0.683  &         0.494        &       -2.401    &        4.970     \\\\\n",
       "\\textbf{X\\_29}          &       0.1943  &        0.943     &     0.206  &         0.837        &       -1.654    &        2.043     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 960400.477 & \\textbf{  Durbin-Watson:     } &       1.983    \\\\\n",
       "\\textbf{Prob(Omnibus):} &    0.000   & \\textbf{  Jarque-Bera (JB):  } & 626204035.997  \\\\\n",
       "\\textbf{Skew:}          &    7.305   & \\textbf{  Prob(JB):          } &        0.00    \\\\\n",
       "\\textbf{Kurtosis:}      &  147.253   & \\textbf{  Cond. No.          } &        17.5    \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors are robust to cluster correlation (cluster)"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:               mobility   R-squared:                       0.011\n",
       "Model:                            OLS   Adj. R-squared:                  0.010\n",
       "Method:                 Least Squares   F-statistic:                       nan\n",
       "Date:                Wed, 21 May 2025   Prob (F-statistic):                nan\n",
       "Time:                        11:31:52   Log-Likelihood:            -3.9202e+06\n",
       "No. Observations:              714897   AIC:                         7.840e+06\n",
       "Df Residuals:                  714831   BIC:                         7.841e+06\n",
       "Df Model:                          65                                         \n",
       "Covariance Type:              cluster                                         \n",
       "==================================================================================\n",
       "                     coef    std err          z      P>|z|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------\n",
       "C(strike)[1]      23.2702      0.807     28.834      0.000      21.688      24.852\n",
       "C(strike)[7]      27.3933      0.814     33.668      0.000      25.799      28.988\n",
       "C(strike)[8]      26.9923      0.815     33.121      0.000      25.395      28.590\n",
       "C(strike)[11]     31.2638      0.815     38.366      0.000      29.667      32.861\n",
       "C(strike)[12]     21.9379      0.805     27.267      0.000      20.361      23.515\n",
       "C(strike)[14]     16.0922      0.816     19.731      0.000      14.494      17.691\n",
       "C(strike)[20]     22.8337      0.812     28.111      0.000      21.242      24.426\n",
       "C(strike)[24]     40.4166      0.819     49.341      0.000      38.811      42.022\n",
       "C(strike)[26]     23.8187      0.785     30.323      0.000      22.279      25.358\n",
       "C(strike)[27]     22.2907      0.816     27.328      0.000      20.692      23.889\n",
       "C(strike)[28]     23.4145      0.816     28.681      0.000      21.814      25.015\n",
       "C(strike)[34]     16.7720      0.812     20.653      0.000      15.180      18.364\n",
       "C(strike)[35]     16.7807      0.815     20.596      0.000      15.184      18.378\n",
       "C(strike)[37]     18.8843      0.816     23.130      0.000      17.284      20.485\n",
       "C(strike)[38]     15.9024      0.812     19.584      0.000      14.311      17.494\n",
       "C(strike)[40]     19.6602      0.814     24.161      0.000      18.065      21.255\n",
       "C(strike)[42]     18.0329      0.813     22.192      0.000      16.440      19.626\n",
       "C(strike)[43]     25.4588      0.816     31.201      0.000      23.860      27.058\n",
       "C(strike)[48]     19.5715      0.811     24.136      0.000      17.982      21.161\n",
       "C(strike)[51]     43.2925      0.814     53.160      0.000      41.696      44.889\n",
       "C(strike)[58]     26.8376      0.811     33.110      0.000      25.249      28.426\n",
       "C(strike)[59]     18.9336      0.815     23.238      0.000      17.337      20.531\n",
       "C(strike)[60]     29.2169      0.805     36.305      0.000      27.640      30.794\n",
       "C(strike)[62]     27.8951      0.812     34.351      0.000      26.304      29.487\n",
       "C(strike)[65]     41.5709      0.810     51.296      0.000      39.982      43.159\n",
       "C(strike)[67]     30.2462      0.803     37.645      0.000      28.672      31.821\n",
       "C(strike)[68]     28.4391      0.802     35.460      0.000      26.867      30.011\n",
       "C(strike)[71]     32.9522      0.812     40.586      0.000      31.361      34.544\n",
       "C(strike)[81]     39.9433      0.810     49.335      0.000      38.356      41.530\n",
       "C(strike)[82]     27.8771      0.813     34.281      0.000      26.283      29.471\n",
       "C(strike)[83]     28.2981      0.812     34.869      0.000      26.707      29.889\n",
       "C(strike)[85]     26.9506      0.811     33.248      0.000      25.362      28.539\n",
       "C(strike)[87]     20.7616      0.814     25.499      0.000      19.166      22.357\n",
       "C(strike)[92]     16.2769      0.813     20.015      0.000      14.683      17.871\n",
       "C(strike)[95]     19.9800      0.817     24.469      0.000      18.380      21.580\n",
       "C(strike)[100]    29.9857      0.813     36.875      0.000      28.392      31.579\n",
       "C(strike)[106]    23.8239      0.748     31.863      0.000      22.358      25.289\n",
       "C(strike)[107]    31.7072      0.701     45.248      0.000      30.334      33.081\n",
       "X_1                1.0739      1.388      0.774      0.439      -1.647       3.794\n",
       "X_2                1.4137      1.403      1.008      0.314      -1.335       4.163\n",
       "X_3                0.2125      1.149      0.185      0.853      -2.040       2.464\n",
       "X_4               -0.0388      0.869     -0.045      0.964      -1.742       1.665\n",
       "X_5                0.8241      0.776      1.062      0.288      -0.697       2.346\n",
       "X_7                0.8414      1.036      0.812      0.417      -1.189       2.871\n",
       "X_8                9.1386      1.940      4.712      0.000       5.337      12.940\n",
       "X_9                4.9364      1.422      3.471      0.001       2.149       7.724\n",
       "X_10               2.4719      1.305      1.894      0.058      -0.085       5.029\n",
       "X_11               2.6319      1.420      1.853      0.064      -0.151       5.415\n",
       "X_12               0.9831      1.031      0.954      0.340      -1.037       3.003\n",
       "X_13               0.2582      0.860      0.300      0.764      -1.428       1.944\n",
       "X_14               1.5257      1.345      1.135      0.257      -1.110       4.161\n",
       "X_15               0.4466      1.044      0.428      0.669      -1.600       2.493\n",
       "X_16               1.2663      1.421      0.891      0.373      -1.519       4.052\n",
       "X_17               0.0291      1.177      0.025      0.980      -2.279       2.337\n",
       "X_18               0.5965      1.334      0.447      0.655      -2.017       3.210\n",
       "X_19               0.0385      1.562      0.025      0.980      -3.022       3.099\n",
       "X_20               0.3541      1.190      0.298      0.766      -1.978       2.687\n",
       "X_21               1.6308      1.446      1.128      0.259      -1.203       4.464\n",
       "X_22              -0.5183      1.454     -0.356      0.721      -3.368       2.331\n",
       "X_23               0.0004      1.281      0.000      1.000      -2.510       2.511\n",
       "X_24               0.4495      1.205      0.373      0.709      -1.913       2.812\n",
       "X_25               0.3535      1.166      0.303      0.762      -1.932       2.639\n",
       "X_26              -0.6282      1.212     -0.518      0.604      -3.003       1.747\n",
       "X_27              -1.9966      1.020     -1.958      0.050      -3.995       0.002\n",
       "X_28               1.2847      1.880      0.683      0.494      -2.401       4.970\n",
       "X_29               0.1943      0.943      0.206      0.837      -1.654       2.043\n",
       "==============================================================================\n",
       "Omnibus:                   960400.477   Durbin-Watson:                   1.983\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):        626204035.997\n",
       "Skew:                           7.305   Prob(JB):                         0.00\n",
       "Kurtosis:                     147.253   Cond. No.                         17.5\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors are robust to cluster correlation (cluster)\n",
       "\"\"\""
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# run the event study regression for mobility around strikes\n",
    "# 7 lags and 21 leads\n",
    "# time vars provide the parameters of interest\n",
    "# drop the day = -2 variable for difference in means and to avoid multicollinearity\n",
    "# use strike fixed effects and cluster SE by strike\n",
    "\n",
    "reg = sm.ols('mobility ~ ' + var_form + ' +  C(strike) - 1', \n",
    "             data=df_low).fit(cov_type='cluster',\n",
    "                          cov_kwds={'groups': df_low['strike'].values})\n",
    "\n",
    "# save parameters and confidence intervals for plotting\n",
    "conf_int = reg.conf_int()\n",
    "res_density_low = pd.DataFrame({\n",
    "    'params': reg.params,\n",
    "    'ci_l': conf_int[0],  # lower bound\n",
    "    'ci_h': conf_int[1],  # upper bound\n",
    "})\n",
    "\n",
    "# print regression summary\n",
    "reg.summary()\n",
    "# reindex the results to go from day = -7 to day = 21\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_two_group(event_res1, event_res2, label1='Group 1', label2='Group 2', \n",
    "                       color1='C0', color2='C1', ylabel='Distance (km)', ylim=[-7,21], xlim=[-7,21],\n",
    "                       outfile='figures/figureXX.pdf'):\n",
    "\n",
    "    plt.figure()\n",
    "    ax = plt.gca()\n",
    "\n",
    "    # Plot for first group\n",
    "    ax.plot(event_res1['day'], event_res1['params'], color=color1, label=label1, zorder=3)\n",
    "    ax.fill_between(event_res1['day'], event_res1['ci_l'], event_res1['ci_h'],\n",
    "                    facecolor=color1, alpha=0.2, zorder=2)\n",
    "\n",
    "    # Plot for second group\n",
    "    ax.plot(event_res2['day'], event_res2['params'], color=color2, label=label2, zorder=3)\n",
    "    ax.fill_between(event_res2['day'], event_res2['ci_l'], event_res2['ci_h'],\n",
    "                    facecolor=color2, alpha=0.2, zorder=2)\n",
    "\n",
    "    # Reference lines\n",
    "    ax.axhline(y=0, color='k', alpha=0.7, linestyle='--', zorder=1)\n",
    "    ax.axvline(x=0, color='k', alpha=0.3, linestyle='--', zorder=1)\n",
    "\n",
    "    # Labels and limits\n",
    "    ax.set_xlim(xlim)\n",
    "    ax.set_ylim(ylim)\n",
    "    ax.set_ylabel(ylabel)\n",
    "    ax.set_xlabel('Days since strike')\n",
    "    ax.legend()\n",
    "\n",
    "    # Save\n",
    "    fig = ax.get_figure()\n",
    "    fig.set_size_inches(10, 6)\n",
    "    fig.savefig(outfile, bbox_inches='tight', format='pdf', dpi=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "event_res_density_high = res_density_high[res_density_high.index.to_series().str.startswith(\"X_\")].copy()\n",
    "event_res_density_high = event_res_density_high.reset_index().rename(columns={'index': 'varname'})\n",
    "event_res_density_high['day'] = event_res_density_high['varname'].str.extract(r'X_(\\d+)').astype(float).astype('Int64') - 8\n",
    "\n",
    "event_res_density_low = res_density_low[res_density_low.index.to_series().str.startswith(\"X_\")].copy()\n",
    "event_res_density_low = event_res_density_low.reset_index().rename(columns={'index': 'varname'})\n",
    "event_res_density_low['day'] = event_res_density_low['varname'].str.extract(r'X_(\\d+)').astype(float).astype('Int64') - 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_two_group(event_res_density_high, event_res_density_low,\n",
    "                       label1='Above Median Population Density',\n",
    "                       label2='Below Median Population Density',\n",
    "                       color1='C0', color2='C1',\n",
    "                       ylabel='Distance (km)',\n",
    "                       outfile='figures/figureX_density.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "strikes['density'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Density coefficient is positive, statistically significant, but quite small. More urban see slightly higher \"mobility\" responses than less urban areas (25th percentile to 75th percentile change is 30 units, so associated change in mobility is .456, which is about 7% of the strike day effect)\n",
    "- The coefficients and errors on the leads and lags are precisely the same, because all we're doing is pulling this strike characteristic out of the FE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure S11A: Mobility Results with Time of Day Subgroups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create separate dataframes for strikes that occur in the morning, afternoon, and evening\n",
    "df_morning = df[df['time_of_day'] == 'morning']\n",
    "df_day = df[df['time_of_day'] == 'day']\n",
    "df_evening = df[df['time_of_day'] == 'evening']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>mobility</td>     <th>  R-squared:         </th>  <td>   0.019</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.019</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>     nan</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 21 May 2025</td> <th>  Prob (F-statistic):</th>   <td>   nan</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>11:46:18</td>     <th>  Log-Likelihood:    </th> <td>-3.0467e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>572793</td>      <th>  AIC:               </th>  <td>6.094e+06</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>572747</td>      <th>  BIC:               </th>  <td>6.094e+06</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    45</td>      <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>       <td>cluster</td>     <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>           <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[7]</th>   <td>   28.9690</td> <td>    1.303</td> <td>   22.238</td> <td> 0.000</td> <td>   26.416</td> <td>   31.522</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[8]</th>   <td>   28.5728</td> <td>    1.304</td> <td>   21.903</td> <td> 0.000</td> <td>   26.016</td> <td>   31.130</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[9]</th>   <td>   20.0463</td> <td>    1.299</td> <td>   15.432</td> <td> 0.000</td> <td>   17.500</td> <td>   22.592</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[11]</th>  <td>   32.8372</td> <td>    1.306</td> <td>   25.150</td> <td> 0.000</td> <td>   30.278</td> <td>   35.396</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[12]</th>  <td>   23.5096</td> <td>    1.276</td> <td>   18.425</td> <td> 0.000</td> <td>   21.009</td> <td>   26.010</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[15]</th>  <td>   29.2066</td> <td>    1.305</td> <td>   22.382</td> <td> 0.000</td> <td>   26.649</td> <td>   31.764</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[28]</th>  <td>   24.9853</td> <td>    1.310</td> <td>   19.068</td> <td> 0.000</td> <td>   22.417</td> <td>   27.553</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[38]</th>  <td>   17.4798</td> <td>    1.298</td> <td>   13.462</td> <td> 0.000</td> <td>   14.935</td> <td>   20.025</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[60]</th>  <td>   30.8156</td> <td>    1.275</td> <td>   24.174</td> <td> 0.000</td> <td>   28.317</td> <td>   33.314</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[65]</th>  <td>   43.1522</td> <td>    1.292</td> <td>   33.398</td> <td> 0.000</td> <td>   40.620</td> <td>   45.685</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[67]</th>  <td>   31.8394</td> <td>    1.272</td> <td>   25.024</td> <td> 0.000</td> <td>   29.346</td> <td>   34.333</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[68]</th>  <td>   30.0370</td> <td>    1.268</td> <td>   23.697</td> <td> 0.000</td> <td>   27.553</td> <td>   32.521</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[69]</th>  <td>   35.5214</td> <td>    1.307</td> <td>   27.187</td> <td> 0.000</td> <td>   32.961</td> <td>   38.082</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[70]</th>  <td>   26.1421</td> <td>    1.306</td> <td>   20.024</td> <td> 0.000</td> <td>   23.583</td> <td>   28.701</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[82]</th>  <td>   29.4535</td> <td>    1.301</td> <td>   22.642</td> <td> 0.000</td> <td>   26.904</td> <td>   32.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[90]</th>  <td>   17.0859</td> <td>    1.305</td> <td>   13.093</td> <td> 0.000</td> <td>   14.528</td> <td>   19.644</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[95]</th>  <td>   21.5622</td> <td>    1.308</td> <td>   16.487</td> <td> 0.000</td> <td>   18.999</td> <td>   24.126</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[106]</th> <td>   25.7172</td> <td>    1.061</td> <td>   24.247</td> <td> 0.000</td> <td>   23.638</td> <td>   27.796</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_1</th>            <td>   -0.8046</td> <td>    0.924</td> <td>   -0.870</td> <td> 0.384</td> <td>   -2.616</td> <td>    1.007</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_2</th>            <td>   -1.6035</td> <td>    1.364</td> <td>   -1.175</td> <td> 0.240</td> <td>   -4.278</td> <td>    1.071</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_3</th>            <td>    0.2590</td> <td>    1.316</td> <td>    0.197</td> <td> 0.844</td> <td>   -2.321</td> <td>    2.839</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_4</th>            <td>   -0.5595</td> <td>    1.046</td> <td>   -0.535</td> <td> 0.593</td> <td>   -2.610</td> <td>    1.491</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_5</th>            <td>   -1.1811</td> <td>    0.432</td> <td>   -2.736</td> <td> 0.006</td> <td>   -2.027</td> <td>   -0.335</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_7</th>            <td>   -0.8801</td> <td>    0.589</td> <td>   -1.495</td> <td> 0.135</td> <td>   -2.034</td> <td>    0.274</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_8</th>            <td>    5.1085</td> <td>    2.528</td> <td>    2.020</td> <td> 0.043</td> <td>    0.153</td> <td>   10.064</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_9</th>            <td>    0.0498</td> <td>    0.794</td> <td>    0.063</td> <td> 0.950</td> <td>   -1.507</td> <td>    1.607</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_10</th>           <td>   -0.2130</td> <td>    1.104</td> <td>   -0.193</td> <td> 0.847</td> <td>   -2.378</td> <td>    1.952</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_11</th>           <td>    0.9294</td> <td>    0.909</td> <td>    1.023</td> <td> 0.306</td> <td>   -0.851</td> <td>    2.710</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_12</th>           <td>   -1.5677</td> <td>    0.799</td> <td>   -1.962</td> <td> 0.050</td> <td>   -3.134</td> <td>   -0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_13</th>           <td>   -0.4887</td> <td>    1.308</td> <td>   -0.374</td> <td> 0.709</td> <td>   -3.053</td> <td>    2.075</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_14</th>           <td>   -1.2587</td> <td>    1.618</td> <td>   -0.778</td> <td> 0.436</td> <td>   -4.429</td> <td>    1.912</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_15</th>           <td>   -1.4370</td> <td>    1.928</td> <td>   -0.745</td> <td> 0.456</td> <td>   -5.217</td> <td>    2.343</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_16</th>           <td>   -0.7298</td> <td>    1.984</td> <td>   -0.368</td> <td> 0.713</td> <td>   -4.618</td> <td>    3.158</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_17</th>           <td>   -0.8431</td> <td>    2.131</td> <td>   -0.396</td> <td> 0.692</td> <td>   -5.020</td> <td>    3.334</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_18</th>           <td>   -0.3966</td> <td>    2.207</td> <td>   -0.180</td> <td> 0.857</td> <td>   -4.722</td> <td>    3.929</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_19</th>           <td>   -2.1942</td> <td>    1.785</td> <td>   -1.229</td> <td> 0.219</td> <td>   -5.693</td> <td>    1.305</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_20</th>           <td>   -2.0015</td> <td>    1.489</td> <td>   -1.345</td> <td> 0.179</td> <td>   -4.919</td> <td>    0.916</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_21</th>           <td>   -0.2695</td> <td>    2.125</td> <td>   -0.127</td> <td> 0.899</td> <td>   -4.435</td> <td>    3.896</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_22</th>           <td>   -1.2801</td> <td>    2.704</td> <td>   -0.473</td> <td> 0.636</td> <td>   -6.579</td> <td>    4.019</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_23</th>           <td>   -1.8910</td> <td>    2.423</td> <td>   -0.780</td> <td> 0.435</td> <td>   -6.640</td> <td>    2.858</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_24</th>           <td>   -0.5772</td> <td>    2.452</td> <td>   -0.235</td> <td> 0.814</td> <td>   -5.382</td> <td>    4.228</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_25</th>           <td>   -0.6969</td> <td>    2.152</td> <td>   -0.324</td> <td> 0.746</td> <td>   -4.914</td> <td>    3.520</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_26</th>           <td>   -1.6841</td> <td>    1.634</td> <td>   -1.031</td> <td> 0.303</td> <td>   -4.887</td> <td>    1.519</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_27</th>           <td>    0.1707</td> <td>    2.447</td> <td>    0.070</td> <td> 0.944</td> <td>   -4.626</td> <td>    4.968</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_28</th>           <td>   -0.4337</td> <td>    2.101</td> <td>   -0.206</td> <td> 0.836</td> <td>   -4.552</td> <td>    3.685</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_29</th>           <td>    1.0276</td> <td>    1.786</td> <td>    0.575</td> <td> 0.565</td> <td>   -2.473</td> <td>    4.528</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>876122.466</td> <th>  Durbin-Watson:     </th>    <td>   1.977</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>   <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>1644740498.882</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>            <td> 9.101</td>   <th>  Prob(JB):          </th>    <td>    0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>        <td>264.884</td>  <th>  Cond. No.          </th>    <td>    29.1</td>   \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors are robust to cluster correlation (cluster)"
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &     mobility     & \\textbf{  R-squared:         } &       0.019     \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &       0.019     \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &         nan     \\\\\n",
       "\\textbf{Date:}             & Wed, 21 May 2025 & \\textbf{  Prob (F-statistic):} &        nan      \\\\\n",
       "\\textbf{Time:}             &     11:46:18     & \\textbf{  Log-Likelihood:    } &  -3.0467e+06    \\\\\n",
       "\\textbf{No. Observations:} &      572793      & \\textbf{  AIC:               } &   6.094e+06     \\\\\n",
       "\\textbf{Df Residuals:}     &      572747      & \\textbf{  BIC:               } &   6.094e+06     \\\\\n",
       "\\textbf{Df Model:}         &          45      & \\textbf{                     } &                 \\\\\n",
       "\\textbf{Covariance Type:}  &     cluster      & \\textbf{                     } &                 \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                        & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{C(strike)[7]}   &      28.9690  &        1.303     &    22.238  &         0.000        &       26.416    &       31.522     \\\\\n",
       "\\textbf{C(strike)[8]}   &      28.5728  &        1.304     &    21.903  &         0.000        &       26.016    &       31.130     \\\\\n",
       "\\textbf{C(strike)[9]}   &      20.0463  &        1.299     &    15.432  &         0.000        &       17.500    &       22.592     \\\\\n",
       "\\textbf{C(strike)[11]}  &      32.8372  &        1.306     &    25.150  &         0.000        &       30.278    &       35.396     \\\\\n",
       "\\textbf{C(strike)[12]}  &      23.5096  &        1.276     &    18.425  &         0.000        &       21.009    &       26.010     \\\\\n",
       "\\textbf{C(strike)[15]}  &      29.2066  &        1.305     &    22.382  &         0.000        &       26.649    &       31.764     \\\\\n",
       "\\textbf{C(strike)[28]}  &      24.9853  &        1.310     &    19.068  &         0.000        &       22.417    &       27.553     \\\\\n",
       "\\textbf{C(strike)[38]}  &      17.4798  &        1.298     &    13.462  &         0.000        &       14.935    &       20.025     \\\\\n",
       "\\textbf{C(strike)[60]}  &      30.8156  &        1.275     &    24.174  &         0.000        &       28.317    &       33.314     \\\\\n",
       "\\textbf{C(strike)[65]}  &      43.1522  &        1.292     &    33.398  &         0.000        &       40.620    &       45.685     \\\\\n",
       "\\textbf{C(strike)[67]}  &      31.8394  &        1.272     &    25.024  &         0.000        &       29.346    &       34.333     \\\\\n",
       "\\textbf{C(strike)[68]}  &      30.0370  &        1.268     &    23.697  &         0.000        &       27.553    &       32.521     \\\\\n",
       "\\textbf{C(strike)[69]}  &      35.5214  &        1.307     &    27.187  &         0.000        &       32.961    &       38.082     \\\\\n",
       "\\textbf{C(strike)[70]}  &      26.1421  &        1.306     &    20.024  &         0.000        &       23.583    &       28.701     \\\\\n",
       "\\textbf{C(strike)[82]}  &      29.4535  &        1.301     &    22.642  &         0.000        &       26.904    &       32.003     \\\\\n",
       "\\textbf{C(strike)[90]}  &      17.0859  &        1.305     &    13.093  &         0.000        &       14.528    &       19.644     \\\\\n",
       "\\textbf{C(strike)[95]}  &      21.5622  &        1.308     &    16.487  &         0.000        &       18.999    &       24.126     \\\\\n",
       "\\textbf{C(strike)[106]} &      25.7172  &        1.061     &    24.247  &         0.000        &       23.638    &       27.796     \\\\\n",
       "\\textbf{X\\_1}           &      -0.8046  &        0.924     &    -0.870  &         0.384        &       -2.616    &        1.007     \\\\\n",
       "\\textbf{X\\_2}           &      -1.6035  &        1.364     &    -1.175  &         0.240        &       -4.278    &        1.071     \\\\\n",
       "\\textbf{X\\_3}           &       0.2590  &        1.316     &     0.197  &         0.844        &       -2.321    &        2.839     \\\\\n",
       "\\textbf{X\\_4}           &      -0.5595  &        1.046     &    -0.535  &         0.593        &       -2.610    &        1.491     \\\\\n",
       "\\textbf{X\\_5}           &      -1.1811  &        0.432     &    -2.736  &         0.006        &       -2.027    &       -0.335     \\\\\n",
       "\\textbf{X\\_7}           &      -0.8801  &        0.589     &    -1.495  &         0.135        &       -2.034    &        0.274     \\\\\n",
       "\\textbf{X\\_8}           &       5.1085  &        2.528     &     2.020  &         0.043        &        0.153    &       10.064     \\\\\n",
       "\\textbf{X\\_9}           &       0.0498  &        0.794     &     0.063  &         0.950        &       -1.507    &        1.607     \\\\\n",
       "\\textbf{X\\_10}          &      -0.2130  &        1.104     &    -0.193  &         0.847        &       -2.378    &        1.952     \\\\\n",
       "\\textbf{X\\_11}          &       0.9294  &        0.909     &     1.023  &         0.306        &       -0.851    &        2.710     \\\\\n",
       "\\textbf{X\\_12}          &      -1.5677  &        0.799     &    -1.962  &         0.050        &       -3.134    &       -0.002     \\\\\n",
       "\\textbf{X\\_13}          &      -0.4887  &        1.308     &    -0.374  &         0.709        &       -3.053    &        2.075     \\\\\n",
       "\\textbf{X\\_14}          &      -1.2587  &        1.618     &    -0.778  &         0.436        &       -4.429    &        1.912     \\\\\n",
       "\\textbf{X\\_15}          &      -1.4370  &        1.928     &    -0.745  &         0.456        &       -5.217    &        2.343     \\\\\n",
       "\\textbf{X\\_16}          &      -0.7298  &        1.984     &    -0.368  &         0.713        &       -4.618    &        3.158     \\\\\n",
       "\\textbf{X\\_17}          &      -0.8431  &        2.131     &    -0.396  &         0.692        &       -5.020    &        3.334     \\\\\n",
       "\\textbf{X\\_18}          &      -0.3966  &        2.207     &    -0.180  &         0.857        &       -4.722    &        3.929     \\\\\n",
       "\\textbf{X\\_19}          &      -2.1942  &        1.785     &    -1.229  &         0.219        &       -5.693    &        1.305     \\\\\n",
       "\\textbf{X\\_20}          &      -2.0015  &        1.489     &    -1.345  &         0.179        &       -4.919    &        0.916     \\\\\n",
       "\\textbf{X\\_21}          &      -0.2695  &        2.125     &    -0.127  &         0.899        &       -4.435    &        3.896     \\\\\n",
       "\\textbf{X\\_22}          &      -1.2801  &        2.704     &    -0.473  &         0.636        &       -6.579    &        4.019     \\\\\n",
       "\\textbf{X\\_23}          &      -1.8910  &        2.423     &    -0.780  &         0.435        &       -6.640    &        2.858     \\\\\n",
       "\\textbf{X\\_24}          &      -0.5772  &        2.452     &    -0.235  &         0.814        &       -5.382    &        4.228     \\\\\n",
       "\\textbf{X\\_25}          &      -0.6969  &        2.152     &    -0.324  &         0.746        &       -4.914    &        3.520     \\\\\n",
       "\\textbf{X\\_26}          &      -1.6841  &        1.634     &    -1.031  &         0.303        &       -4.887    &        1.519     \\\\\n",
       "\\textbf{X\\_27}          &       0.1707  &        2.447     &     0.070  &         0.944        &       -4.626    &        4.968     \\\\\n",
       "\\textbf{X\\_28}          &      -0.4337  &        2.101     &    -0.206  &         0.836        &       -4.552    &        3.685     \\\\\n",
       "\\textbf{X\\_29}          &       1.0276  &        1.786     &     0.575  &         0.565        &       -2.473    &        4.528     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 876122.466 & \\textbf{  Durbin-Watson:     } &       1.977     \\\\\n",
       "\\textbf{Prob(Omnibus):} &    0.000   & \\textbf{  Jarque-Bera (JB):  } & 1644740498.882  \\\\\n",
       "\\textbf{Skew:}          &    9.101   & \\textbf{  Prob(JB):          } &        0.00     \\\\\n",
       "\\textbf{Kurtosis:}      &  264.884   & \\textbf{  Cond. No.          } &        29.1     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors are robust to cluster correlation (cluster)"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:               mobility   R-squared:                       0.019\n",
       "Model:                            OLS   Adj. R-squared:                  0.019\n",
       "Method:                 Least Squares   F-statistic:                       nan\n",
       "Date:                Wed, 21 May 2025   Prob (F-statistic):                nan\n",
       "Time:                        11:46:18   Log-Likelihood:            -3.0467e+06\n",
       "No. Observations:              572793   AIC:                         6.094e+06\n",
       "Df Residuals:                  572747   BIC:                         6.094e+06\n",
       "Df Model:                          45                                         \n",
       "Covariance Type:              cluster                                         \n",
       "==================================================================================\n",
       "                     coef    std err          z      P>|z|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------\n",
       "C(strike)[7]      28.9690      1.303     22.238      0.000      26.416      31.522\n",
       "C(strike)[8]      28.5728      1.304     21.903      0.000      26.016      31.130\n",
       "C(strike)[9]      20.0463      1.299     15.432      0.000      17.500      22.592\n",
       "C(strike)[11]     32.8372      1.306     25.150      0.000      30.278      35.396\n",
       "C(strike)[12]     23.5096      1.276     18.425      0.000      21.009      26.010\n",
       "C(strike)[15]     29.2066      1.305     22.382      0.000      26.649      31.764\n",
       "C(strike)[28]     24.9853      1.310     19.068      0.000      22.417      27.553\n",
       "C(strike)[38]     17.4798      1.298     13.462      0.000      14.935      20.025\n",
       "C(strike)[60]     30.8156      1.275     24.174      0.000      28.317      33.314\n",
       "C(strike)[65]     43.1522      1.292     33.398      0.000      40.620      45.685\n",
       "C(strike)[67]     31.8394      1.272     25.024      0.000      29.346      34.333\n",
       "C(strike)[68]     30.0370      1.268     23.697      0.000      27.553      32.521\n",
       "C(strike)[69]     35.5214      1.307     27.187      0.000      32.961      38.082\n",
       "C(strike)[70]     26.1421      1.306     20.024      0.000      23.583      28.701\n",
       "C(strike)[82]     29.4535      1.301     22.642      0.000      26.904      32.003\n",
       "C(strike)[90]     17.0859      1.305     13.093      0.000      14.528      19.644\n",
       "C(strike)[95]     21.5622      1.308     16.487      0.000      18.999      24.126\n",
       "C(strike)[106]    25.7172      1.061     24.247      0.000      23.638      27.796\n",
       "X_1               -0.8046      0.924     -0.870      0.384      -2.616       1.007\n",
       "X_2               -1.6035      1.364     -1.175      0.240      -4.278       1.071\n",
       "X_3                0.2590      1.316      0.197      0.844      -2.321       2.839\n",
       "X_4               -0.5595      1.046     -0.535      0.593      -2.610       1.491\n",
       "X_5               -1.1811      0.432     -2.736      0.006      -2.027      -0.335\n",
       "X_7               -0.8801      0.589     -1.495      0.135      -2.034       0.274\n",
       "X_8                5.1085      2.528      2.020      0.043       0.153      10.064\n",
       "X_9                0.0498      0.794      0.063      0.950      -1.507       1.607\n",
       "X_10              -0.2130      1.104     -0.193      0.847      -2.378       1.952\n",
       "X_11               0.9294      0.909      1.023      0.306      -0.851       2.710\n",
       "X_12              -1.5677      0.799     -1.962      0.050      -3.134      -0.002\n",
       "X_13              -0.4887      1.308     -0.374      0.709      -3.053       2.075\n",
       "X_14              -1.2587      1.618     -0.778      0.436      -4.429       1.912\n",
       "X_15              -1.4370      1.928     -0.745      0.456      -5.217       2.343\n",
       "X_16              -0.7298      1.984     -0.368      0.713      -4.618       3.158\n",
       "X_17              -0.8431      2.131     -0.396      0.692      -5.020       3.334\n",
       "X_18              -0.3966      2.207     -0.180      0.857      -4.722       3.929\n",
       "X_19              -2.1942      1.785     -1.229      0.219      -5.693       1.305\n",
       "X_20              -2.0015      1.489     -1.345      0.179      -4.919       0.916\n",
       "X_21              -0.2695      2.125     -0.127      0.899      -4.435       3.896\n",
       "X_22              -1.2801      2.704     -0.473      0.636      -6.579       4.019\n",
       "X_23              -1.8910      2.423     -0.780      0.435      -6.640       2.858\n",
       "X_24              -0.5772      2.452     -0.235      0.814      -5.382       4.228\n",
       "X_25              -0.6969      2.152     -0.324      0.746      -4.914       3.520\n",
       "X_26              -1.6841      1.634     -1.031      0.303      -4.887       1.519\n",
       "X_27               0.1707      2.447      0.070      0.944      -4.626       4.968\n",
       "X_28              -0.4337      2.101     -0.206      0.836      -4.552       3.685\n",
       "X_29               1.0276      1.786      0.575      0.565      -2.473       4.528\n",
       "==============================================================================\n",
       "Omnibus:                   876122.466   Durbin-Watson:                   1.977\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):       1644740498.882\n",
       "Skew:                           9.101   Prob(JB):                         0.00\n",
       "Kurtosis:                     264.884   Cond. No.                         29.1\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors are robust to cluster correlation (cluster)\n",
       "\"\"\""
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# run the event study regression for mobility around strikes\n",
    "# 7 lags and 21 leads\n",
    "# time vars provide the parameters of interest\n",
    "# drop the day = -2 variable for difference in means and to avoid multicollinearity\n",
    "# use strike fixed effects and cluster SE by strike\n",
    "\n",
    "reg = sm.ols('mobility ~ ' + var_form + ' +  C(strike) - 1 ', \n",
    "             data=df_morning).fit(cov_type='cluster',\n",
    "                          cov_kwds={'groups': df_morning['strike'].values})\n",
    "\n",
    "# save parameters and confidence intervals for plotting\n",
    "conf_int = reg.conf_int()\n",
    "res_morning = pd.DataFrame({\n",
    "    'params': reg.params,\n",
    "    'ci_l': conf_int[0],  # lower bound\n",
    "    'ci_h': conf_int[1],  # upper bound\n",
    "})\n",
    "\n",
    "# print regression summary\n",
    "reg.summary()\n",
    "# reindex the results to go from day = -7 to day = 21\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>mobility</td>     <th>  R-squared:         </th>  <td>   0.015</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.015</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>     nan</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 21 May 2025</td> <th>  Prob (F-statistic):</th>   <td>   nan</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>11:46:38</td>     <th>  Log-Likelihood:    </th> <td>-6.8061e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>1107009</td>     <th>  AIC:               </th>  <td>1.361e+07</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>1106953</td>     <th>  BIC:               </th>  <td>1.361e+07</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    55</td>      <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>       <td>cluster</td>     <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>           <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[1]</th>   <td>   22.4660</td> <td>    0.922</td> <td>   24.372</td> <td> 0.000</td> <td>   20.659</td> <td>   24.273</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[3]</th>   <td>   23.5301</td> <td>    0.968</td> <td>   24.314</td> <td> 0.000</td> <td>   21.633</td> <td>   25.427</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[4]</th>   <td>   28.9951</td> <td>    0.938</td> <td>   30.911</td> <td> 0.000</td> <td>   27.157</td> <td>   30.834</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[6]</th>   <td>   31.2897</td> <td>    0.948</td> <td>   33.011</td> <td> 0.000</td> <td>   29.432</td> <td>   33.147</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[10]</th>  <td>   21.6259</td> <td>    0.947</td> <td>   22.831</td> <td> 0.000</td> <td>   19.769</td> <td>   23.482</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[14]</th>  <td>   15.1755</td> <td>    0.980</td> <td>   15.489</td> <td> 0.000</td> <td>   13.255</td> <td>   17.096</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[20]</th>  <td>   21.9906</td> <td>    0.934</td> <td>   23.544</td> <td> 0.000</td> <td>   20.160</td> <td>   23.821</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[31]</th>  <td>   17.2100</td> <td>    0.952</td> <td>   18.087</td> <td> 0.000</td> <td>   15.345</td> <td>   19.075</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[34]</th>  <td>   15.9326</td> <td>    0.931</td> <td>   17.121</td> <td> 0.000</td> <td>   14.109</td> <td>   17.757</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[37]</th>  <td>   18.0121</td> <td>    0.945</td> <td>   19.058</td> <td> 0.000</td> <td>   16.160</td> <td>   19.864</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[39]</th>  <td>   21.3103</td> <td>    0.948</td> <td>   22.475</td> <td> 0.000</td> <td>   19.452</td> <td>   23.169</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[40]</th>  <td>   18.7971</td> <td>    0.956</td> <td>   19.667</td> <td> 0.000</td> <td>   16.924</td> <td>   20.670</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[42]</th>  <td>   17.1660</td> <td>    0.955</td> <td>   17.977</td> <td> 0.000</td> <td>   15.294</td> <td>   19.037</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[43]</th>  <td>   24.5806</td> <td>    0.953</td> <td>   25.802</td> <td> 0.000</td> <td>   22.713</td> <td>   26.448</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[47]</th>  <td>   34.0795</td> <td>    0.930</td> <td>   36.641</td> <td> 0.000</td> <td>   32.257</td> <td>   35.902</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[49]</th>  <td>   30.4473</td> <td>    0.944</td> <td>   32.261</td> <td> 0.000</td> <td>   28.597</td> <td>   32.297</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[57]</th>  <td>   31.1298</td> <td>    0.945</td> <td>   32.931</td> <td> 0.000</td> <td>   29.277</td> <td>   32.983</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[58]</th>  <td>   26.0037</td> <td>    0.937</td> <td>   27.746</td> <td> 0.000</td> <td>   24.167</td> <td>   27.841</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[59]</th>  <td>   18.0565</td> <td>    0.956</td> <td>   18.891</td> <td> 0.000</td> <td>   16.183</td> <td>   19.930</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[62]</th>  <td>   27.0336</td> <td>    0.956</td> <td>   28.283</td> <td> 0.000</td> <td>   25.160</td> <td>   28.907</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[71]</th>  <td>   32.1175</td> <td>    0.934</td> <td>   34.394</td> <td> 0.000</td> <td>   30.287</td> <td>   33.948</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[72]</th>  <td>   34.1914</td> <td>    0.947</td> <td>   36.117</td> <td> 0.000</td> <td>   32.336</td> <td>   36.047</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[75]</th>  <td>   39.7377</td> <td>    0.951</td> <td>   41.795</td> <td> 0.000</td> <td>   37.874</td> <td>   41.601</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[76]</th>  <td>   62.0523</td> <td>    0.955</td> <td>   64.959</td> <td> 0.000</td> <td>   60.180</td> <td>   63.925</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[83]</th>  <td>   27.4578</td> <td>    0.935</td> <td>   29.364</td> <td> 0.000</td> <td>   25.625</td> <td>   29.291</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[85]</th>  <td>   26.1195</td> <td>    0.931</td> <td>   28.058</td> <td> 0.000</td> <td>   24.295</td> <td>   27.944</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[87]</th>  <td>   19.8930</td> <td>    0.950</td> <td>   20.951</td> <td> 0.000</td> <td>   18.032</td> <td>   21.754</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[107]</th> <td>   31.9307</td> <td>    0.626</td> <td>   51.038</td> <td> 0.000</td> <td>   30.704</td> <td>   33.157</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_1</th>            <td>   -5.0727</td> <td>    4.505</td> <td>   -1.126</td> <td> 0.260</td> <td>  -13.903</td> <td>    3.757</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_2</th>            <td>   -4.9414</td> <td>    4.857</td> <td>   -1.017</td> <td> 0.309</td> <td>  -14.461</td> <td>    4.578</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_3</th>            <td>   -5.3335</td> <td>    4.650</td> <td>   -1.147</td> <td> 0.251</td> <td>  -14.448</td> <td>    3.781</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_4</th>            <td>   -2.6961</td> <td>    2.516</td> <td>   -1.072</td> <td> 0.284</td> <td>   -7.627</td> <td>    2.235</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_5</th>            <td>    0.2915</td> <td>    0.857</td> <td>    0.340</td> <td> 0.734</td> <td>   -1.388</td> <td>    1.971</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_7</th>            <td>   -5.0087</td> <td>    4.790</td> <td>   -1.046</td> <td> 0.296</td> <td>  -14.397</td> <td>    4.379</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_8</th>            <td>    9.1334</td> <td>    3.028</td> <td>    3.016</td> <td> 0.003</td> <td>    3.198</td> <td>   15.069</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_9</th>            <td>    5.4109</td> <td>    2.760</td> <td>    1.960</td> <td> 0.050</td> <td>    0.001</td> <td>   10.821</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_10</th>           <td>    3.8498</td> <td>    2.722</td> <td>    1.414</td> <td> 0.157</td> <td>   -1.485</td> <td>    9.185</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_11</th>           <td>    6.6419</td> <td>    4.753</td> <td>    1.397</td> <td> 0.162</td> <td>   -2.674</td> <td>   15.958</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_12</th>           <td>    0.2329</td> <td>    1.280</td> <td>    0.182</td> <td> 0.856</td> <td>   -2.277</td> <td>    2.742</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_13</th>           <td>    2.0949</td> <td>    1.407</td> <td>    1.488</td> <td> 0.137</td> <td>   -0.664</td> <td>    4.853</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_14</th>           <td>    2.6839</td> <td>    1.673</td> <td>    1.604</td> <td> 0.109</td> <td>   -0.595</td> <td>    5.963</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_15</th>           <td>    3.6926</td> <td>    3.185</td> <td>    1.159</td> <td> 0.246</td> <td>   -2.551</td> <td>    9.936</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_16</th>           <td>    5.3261</td> <td>    3.682</td> <td>    1.447</td> <td> 0.148</td> <td>   -1.891</td> <td>   12.543</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_17</th>           <td>    4.4504</td> <td>    3.188</td> <td>    1.396</td> <td> 0.163</td> <td>   -1.797</td> <td>   10.698</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_18</th>           <td>    2.3218</td> <td>    1.406</td> <td>    1.651</td> <td> 0.099</td> <td>   -0.435</td> <td>    5.078</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_19</th>           <td>    0.6112</td> <td>    1.494</td> <td>    0.409</td> <td> 0.683</td> <td>   -2.317</td> <td>    3.540</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_20</th>           <td>    4.1906</td> <td>    1.889</td> <td>    2.219</td> <td> 0.026</td> <td>    0.489</td> <td>    7.892</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_21</th>           <td>    3.9135</td> <td>    1.949</td> <td>    2.008</td> <td> 0.045</td> <td>    0.094</td> <td>    7.733</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_22</th>           <td>    2.9965</td> <td>    2.026</td> <td>    1.479</td> <td> 0.139</td> <td>   -0.975</td> <td>    6.968</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_23</th>           <td>    3.9643</td> <td>    2.098</td> <td>    1.890</td> <td> 0.059</td> <td>   -0.147</td> <td>    8.075</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_24</th>           <td>    3.7416</td> <td>    1.994</td> <td>    1.876</td> <td> 0.061</td> <td>   -0.167</td> <td>    7.651</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_25</th>           <td>    2.7686</td> <td>    1.513</td> <td>    1.829</td> <td> 0.067</td> <td>   -0.198</td> <td>    5.735</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_26</th>           <td>    1.1291</td> <td>    1.091</td> <td>    1.035</td> <td> 0.301</td> <td>   -1.009</td> <td>    3.267</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_27</th>           <td>    3.1006</td> <td>    1.863</td> <td>    1.664</td> <td> 0.096</td> <td>   -0.551</td> <td>    6.753</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_28</th>           <td>    3.7905</td> <td>    2.387</td> <td>    1.588</td> <td> 0.112</td> <td>   -0.888</td> <td>    8.469</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_29</th>           <td>    1.9402</td> <td>    1.243</td> <td>    1.561</td> <td> 0.118</td> <td>   -0.496</td> <td>    4.376</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>2075859.435</td> <th>  Durbin-Watson:     </th>    <td>   1.988</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>   <td> 0.000</td>    <th>  Jarque-Bera (JB):  </th> <td>4541009675.356</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>            <td>14.321</td>    <th>  Prob(JB):          </th>    <td>    0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>        <td>315.456</td>   <th>  Cond. No.          </th>    <td>    21.0</td>   \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors are robust to cluster correlation (cluster)"
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &     mobility     & \\textbf{  R-squared:         } &       0.015     \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &       0.015     \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &         nan     \\\\\n",
       "\\textbf{Date:}             & Wed, 21 May 2025 & \\textbf{  Prob (F-statistic):} &        nan      \\\\\n",
       "\\textbf{Time:}             &     11:46:38     & \\textbf{  Log-Likelihood:    } &  -6.8061e+06    \\\\\n",
       "\\textbf{No. Observations:} &     1107009      & \\textbf{  AIC:               } &   1.361e+07     \\\\\n",
       "\\textbf{Df Residuals:}     &     1106953      & \\textbf{  BIC:               } &   1.361e+07     \\\\\n",
       "\\textbf{Df Model:}         &          55      & \\textbf{                     } &                 \\\\\n",
       "\\textbf{Covariance Type:}  &     cluster      & \\textbf{                     } &                 \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                        & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{C(strike)[1]}   &      22.4660  &        0.922     &    24.372  &         0.000        &       20.659    &       24.273     \\\\\n",
       "\\textbf{C(strike)[3]}   &      23.5301  &        0.968     &    24.314  &         0.000        &       21.633    &       25.427     \\\\\n",
       "\\textbf{C(strike)[4]}   &      28.9951  &        0.938     &    30.911  &         0.000        &       27.157    &       30.834     \\\\\n",
       "\\textbf{C(strike)[6]}   &      31.2897  &        0.948     &    33.011  &         0.000        &       29.432    &       33.147     \\\\\n",
       "\\textbf{C(strike)[10]}  &      21.6259  &        0.947     &    22.831  &         0.000        &       19.769    &       23.482     \\\\\n",
       "\\textbf{C(strike)[14]}  &      15.1755  &        0.980     &    15.489  &         0.000        &       13.255    &       17.096     \\\\\n",
       "\\textbf{C(strike)[20]}  &      21.9906  &        0.934     &    23.544  &         0.000        &       20.160    &       23.821     \\\\\n",
       "\\textbf{C(strike)[31]}  &      17.2100  &        0.952     &    18.087  &         0.000        &       15.345    &       19.075     \\\\\n",
       "\\textbf{C(strike)[34]}  &      15.9326  &        0.931     &    17.121  &         0.000        &       14.109    &       17.757     \\\\\n",
       "\\textbf{C(strike)[37]}  &      18.0121  &        0.945     &    19.058  &         0.000        &       16.160    &       19.864     \\\\\n",
       "\\textbf{C(strike)[39]}  &      21.3103  &        0.948     &    22.475  &         0.000        &       19.452    &       23.169     \\\\\n",
       "\\textbf{C(strike)[40]}  &      18.7971  &        0.956     &    19.667  &         0.000        &       16.924    &       20.670     \\\\\n",
       "\\textbf{C(strike)[42]}  &      17.1660  &        0.955     &    17.977  &         0.000        &       15.294    &       19.037     \\\\\n",
       "\\textbf{C(strike)[43]}  &      24.5806  &        0.953     &    25.802  &         0.000        &       22.713    &       26.448     \\\\\n",
       "\\textbf{C(strike)[47]}  &      34.0795  &        0.930     &    36.641  &         0.000        &       32.257    &       35.902     \\\\\n",
       "\\textbf{C(strike)[49]}  &      30.4473  &        0.944     &    32.261  &         0.000        &       28.597    &       32.297     \\\\\n",
       "\\textbf{C(strike)[57]}  &      31.1298  &        0.945     &    32.931  &         0.000        &       29.277    &       32.983     \\\\\n",
       "\\textbf{C(strike)[58]}  &      26.0037  &        0.937     &    27.746  &         0.000        &       24.167    &       27.841     \\\\\n",
       "\\textbf{C(strike)[59]}  &      18.0565  &        0.956     &    18.891  &         0.000        &       16.183    &       19.930     \\\\\n",
       "\\textbf{C(strike)[62]}  &      27.0336  &        0.956     &    28.283  &         0.000        &       25.160    &       28.907     \\\\\n",
       "\\textbf{C(strike)[71]}  &      32.1175  &        0.934     &    34.394  &         0.000        &       30.287    &       33.948     \\\\\n",
       "\\textbf{C(strike)[72]}  &      34.1914  &        0.947     &    36.117  &         0.000        &       32.336    &       36.047     \\\\\n",
       "\\textbf{C(strike)[75]}  &      39.7377  &        0.951     &    41.795  &         0.000        &       37.874    &       41.601     \\\\\n",
       "\\textbf{C(strike)[76]}  &      62.0523  &        0.955     &    64.959  &         0.000        &       60.180    &       63.925     \\\\\n",
       "\\textbf{C(strike)[83]}  &      27.4578  &        0.935     &    29.364  &         0.000        &       25.625    &       29.291     \\\\\n",
       "\\textbf{C(strike)[85]}  &      26.1195  &        0.931     &    28.058  &         0.000        &       24.295    &       27.944     \\\\\n",
       "\\textbf{C(strike)[87]}  &      19.8930  &        0.950     &    20.951  &         0.000        &       18.032    &       21.754     \\\\\n",
       "\\textbf{C(strike)[107]} &      31.9307  &        0.626     &    51.038  &         0.000        &       30.704    &       33.157     \\\\\n",
       "\\textbf{X\\_1}           &      -5.0727  &        4.505     &    -1.126  &         0.260        &      -13.903    &        3.757     \\\\\n",
       "\\textbf{X\\_2}           &      -4.9414  &        4.857     &    -1.017  &         0.309        &      -14.461    &        4.578     \\\\\n",
       "\\textbf{X\\_3}           &      -5.3335  &        4.650     &    -1.147  &         0.251        &      -14.448    &        3.781     \\\\\n",
       "\\textbf{X\\_4}           &      -2.6961  &        2.516     &    -1.072  &         0.284        &       -7.627    &        2.235     \\\\\n",
       "\\textbf{X\\_5}           &       0.2915  &        0.857     &     0.340  &         0.734        &       -1.388    &        1.971     \\\\\n",
       "\\textbf{X\\_7}           &      -5.0087  &        4.790     &    -1.046  &         0.296        &      -14.397    &        4.379     \\\\\n",
       "\\textbf{X\\_8}           &       9.1334  &        3.028     &     3.016  &         0.003        &        3.198    &       15.069     \\\\\n",
       "\\textbf{X\\_9}           &       5.4109  &        2.760     &     1.960  &         0.050        &        0.001    &       10.821     \\\\\n",
       "\\textbf{X\\_10}          &       3.8498  &        2.722     &     1.414  &         0.157        &       -1.485    &        9.185     \\\\\n",
       "\\textbf{X\\_11}          &       6.6419  &        4.753     &     1.397  &         0.162        &       -2.674    &       15.958     \\\\\n",
       "\\textbf{X\\_12}          &       0.2329  &        1.280     &     0.182  &         0.856        &       -2.277    &        2.742     \\\\\n",
       "\\textbf{X\\_13}          &       2.0949  &        1.407     &     1.488  &         0.137        &       -0.664    &        4.853     \\\\\n",
       "\\textbf{X\\_14}          &       2.6839  &        1.673     &     1.604  &         0.109        &       -0.595    &        5.963     \\\\\n",
       "\\textbf{X\\_15}          &       3.6926  &        3.185     &     1.159  &         0.246        &       -2.551    &        9.936     \\\\\n",
       "\\textbf{X\\_16}          &       5.3261  &        3.682     &     1.447  &         0.148        &       -1.891    &       12.543     \\\\\n",
       "\\textbf{X\\_17}          &       4.4504  &        3.188     &     1.396  &         0.163        &       -1.797    &       10.698     \\\\\n",
       "\\textbf{X\\_18}          &       2.3218  &        1.406     &     1.651  &         0.099        &       -0.435    &        5.078     \\\\\n",
       "\\textbf{X\\_19}          &       0.6112  &        1.494     &     0.409  &         0.683        &       -2.317    &        3.540     \\\\\n",
       "\\textbf{X\\_20}          &       4.1906  &        1.889     &     2.219  &         0.026        &        0.489    &        7.892     \\\\\n",
       "\\textbf{X\\_21}          &       3.9135  &        1.949     &     2.008  &         0.045        &        0.094    &        7.733     \\\\\n",
       "\\textbf{X\\_22}          &       2.9965  &        2.026     &     1.479  &         0.139        &       -0.975    &        6.968     \\\\\n",
       "\\textbf{X\\_23}          &       3.9643  &        2.098     &     1.890  &         0.059        &       -0.147    &        8.075     \\\\\n",
       "\\textbf{X\\_24}          &       3.7416  &        1.994     &     1.876  &         0.061        &       -0.167    &        7.651     \\\\\n",
       "\\textbf{X\\_25}          &       2.7686  &        1.513     &     1.829  &         0.067        &       -0.198    &        5.735     \\\\\n",
       "\\textbf{X\\_26}          &       1.1291  &        1.091     &     1.035  &         0.301        &       -1.009    &        3.267     \\\\\n",
       "\\textbf{X\\_27}          &       3.1006  &        1.863     &     1.664  &         0.096        &       -0.551    &        6.753     \\\\\n",
       "\\textbf{X\\_28}          &       3.7905  &        2.387     &     1.588  &         0.112        &       -0.888    &        8.469     \\\\\n",
       "\\textbf{X\\_29}          &       1.9402  &        1.243     &     1.561  &         0.118        &       -0.496    &        4.376     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 2075859.435 & \\textbf{  Durbin-Watson:     } &       1.988     \\\\\n",
       "\\textbf{Prob(Omnibus):} &     0.000   & \\textbf{  Jarque-Bera (JB):  } & 4541009675.356  \\\\\n",
       "\\textbf{Skew:}          &    14.321   & \\textbf{  Prob(JB):          } &        0.00     \\\\\n",
       "\\textbf{Kurtosis:}      &   315.456   & \\textbf{  Cond. No.          } &        21.0     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors are robust to cluster correlation (cluster)"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:               mobility   R-squared:                       0.015\n",
       "Model:                            OLS   Adj. R-squared:                  0.015\n",
       "Method:                 Least Squares   F-statistic:                       nan\n",
       "Date:                Wed, 21 May 2025   Prob (F-statistic):                nan\n",
       "Time:                        11:46:38   Log-Likelihood:            -6.8061e+06\n",
       "No. Observations:             1107009   AIC:                         1.361e+07\n",
       "Df Residuals:                 1106953   BIC:                         1.361e+07\n",
       "Df Model:                          55                                         \n",
       "Covariance Type:              cluster                                         \n",
       "==================================================================================\n",
       "                     coef    std err          z      P>|z|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------\n",
       "C(strike)[1]      22.4660      0.922     24.372      0.000      20.659      24.273\n",
       "C(strike)[3]      23.5301      0.968     24.314      0.000      21.633      25.427\n",
       "C(strike)[4]      28.9951      0.938     30.911      0.000      27.157      30.834\n",
       "C(strike)[6]      31.2897      0.948     33.011      0.000      29.432      33.147\n",
       "C(strike)[10]     21.6259      0.947     22.831      0.000      19.769      23.482\n",
       "C(strike)[14]     15.1755      0.980     15.489      0.000      13.255      17.096\n",
       "C(strike)[20]     21.9906      0.934     23.544      0.000      20.160      23.821\n",
       "C(strike)[31]     17.2100      0.952     18.087      0.000      15.345      19.075\n",
       "C(strike)[34]     15.9326      0.931     17.121      0.000      14.109      17.757\n",
       "C(strike)[37]     18.0121      0.945     19.058      0.000      16.160      19.864\n",
       "C(strike)[39]     21.3103      0.948     22.475      0.000      19.452      23.169\n",
       "C(strike)[40]     18.7971      0.956     19.667      0.000      16.924      20.670\n",
       "C(strike)[42]     17.1660      0.955     17.977      0.000      15.294      19.037\n",
       "C(strike)[43]     24.5806      0.953     25.802      0.000      22.713      26.448\n",
       "C(strike)[47]     34.0795      0.930     36.641      0.000      32.257      35.902\n",
       "C(strike)[49]     30.4473      0.944     32.261      0.000      28.597      32.297\n",
       "C(strike)[57]     31.1298      0.945     32.931      0.000      29.277      32.983\n",
       "C(strike)[58]     26.0037      0.937     27.746      0.000      24.167      27.841\n",
       "C(strike)[59]     18.0565      0.956     18.891      0.000      16.183      19.930\n",
       "C(strike)[62]     27.0336      0.956     28.283      0.000      25.160      28.907\n",
       "C(strike)[71]     32.1175      0.934     34.394      0.000      30.287      33.948\n",
       "C(strike)[72]     34.1914      0.947     36.117      0.000      32.336      36.047\n",
       "C(strike)[75]     39.7377      0.951     41.795      0.000      37.874      41.601\n",
       "C(strike)[76]     62.0523      0.955     64.959      0.000      60.180      63.925\n",
       "C(strike)[83]     27.4578      0.935     29.364      0.000      25.625      29.291\n",
       "C(strike)[85]     26.1195      0.931     28.058      0.000      24.295      27.944\n",
       "C(strike)[87]     19.8930      0.950     20.951      0.000      18.032      21.754\n",
       "C(strike)[107]    31.9307      0.626     51.038      0.000      30.704      33.157\n",
       "X_1               -5.0727      4.505     -1.126      0.260     -13.903       3.757\n",
       "X_2               -4.9414      4.857     -1.017      0.309     -14.461       4.578\n",
       "X_3               -5.3335      4.650     -1.147      0.251     -14.448       3.781\n",
       "X_4               -2.6961      2.516     -1.072      0.284      -7.627       2.235\n",
       "X_5                0.2915      0.857      0.340      0.734      -1.388       1.971\n",
       "X_7               -5.0087      4.790     -1.046      0.296     -14.397       4.379\n",
       "X_8                9.1334      3.028      3.016      0.003       3.198      15.069\n",
       "X_9                5.4109      2.760      1.960      0.050       0.001      10.821\n",
       "X_10               3.8498      2.722      1.414      0.157      -1.485       9.185\n",
       "X_11               6.6419      4.753      1.397      0.162      -2.674      15.958\n",
       "X_12               0.2329      1.280      0.182      0.856      -2.277       2.742\n",
       "X_13               2.0949      1.407      1.488      0.137      -0.664       4.853\n",
       "X_14               2.6839      1.673      1.604      0.109      -0.595       5.963\n",
       "X_15               3.6926      3.185      1.159      0.246      -2.551       9.936\n",
       "X_16               5.3261      3.682      1.447      0.148      -1.891      12.543\n",
       "X_17               4.4504      3.188      1.396      0.163      -1.797      10.698\n",
       "X_18               2.3218      1.406      1.651      0.099      -0.435       5.078\n",
       "X_19               0.6112      1.494      0.409      0.683      -2.317       3.540\n",
       "X_20               4.1906      1.889      2.219      0.026       0.489       7.892\n",
       "X_21               3.9135      1.949      2.008      0.045       0.094       7.733\n",
       "X_22               2.9965      2.026      1.479      0.139      -0.975       6.968\n",
       "X_23               3.9643      2.098      1.890      0.059      -0.147       8.075\n",
       "X_24               3.7416      1.994      1.876      0.061      -0.167       7.651\n",
       "X_25               2.7686      1.513      1.829      0.067      -0.198       5.735\n",
       "X_26               1.1291      1.091      1.035      0.301      -1.009       3.267\n",
       "X_27               3.1006      1.863      1.664      0.096      -0.551       6.753\n",
       "X_28               3.7905      2.387      1.588      0.112      -0.888       8.469\n",
       "X_29               1.9402      1.243      1.561      0.118      -0.496       4.376\n",
       "==============================================================================\n",
       "Omnibus:                  2075859.435   Durbin-Watson:                   1.988\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):       4541009675.356\n",
       "Skew:                          14.321   Prob(JB):                         0.00\n",
       "Kurtosis:                     315.456   Cond. No.                         21.0\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors are robust to cluster correlation (cluster)\n",
       "\"\"\""
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# run the event study regression for mobility around strikes\n",
    "# 7 lags and 21 leads\n",
    "# time vars provide the parameters of interest\n",
    "# drop the day = -2 variable for difference in means and to avoid multicollinearity\n",
    "# use strike fixed effects and cluster SE by strike\n",
    "\n",
    "reg = sm.ols('mobility ~ ' + var_form + ' +  C(strike) - 1 ', \n",
    "             data=df_day).fit(cov_type='cluster',\n",
    "                          cov_kwds={'groups': df_day['strike'].values})\n",
    "\n",
    "# save parameters and confidence intervals for plotting\n",
    "conf_int = reg.conf_int()\n",
    "res_day = pd.DataFrame({\n",
    "    'params': reg.params,\n",
    "    'ci_l': conf_int[0],  # lower bound\n",
    "    'ci_h': conf_int[1],  # upper bound\n",
    "})\n",
    "\n",
    "# print regression summary\n",
    "reg.summary()\n",
    "# reindex the results to go from day = -7 to day = 21\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/statsmodels/base/model.py:1894: ValueWarning: covariance of constraints does not have full rank. The number of constraints is 55, but rank is 27\n",
      "  warnings.warn('covariance of constraints does not have full '\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>mobility</td>     <th>  R-squared:         </th>  <td>   0.038</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.038</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>   1326.</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 21 May 2025</td> <th>  Prob (F-statistic):</th>  <td>7.01e-36</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>11:46:57</td>     <th>  Log-Likelihood:    </th> <td>-9.2249e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>1700832</td>     <th>  AIC:               </th>  <td>1.845e+07</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>1700776</td>     <th>  BIC:               </th>  <td>1.845e+07</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    55</td>      <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>       <td>cluster</td>     <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "            <td></td>              <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>time_of_day[evening]</th> <td>   27.5899</td> <td>    0.458</td> <td>   60.215</td> <td> 0.000</td> <td>   26.692</td> <td>   28.488</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[T.17]</th>      <td>   -5.8809</td> <td>    0.003</td> <td>-1917.523</td> <td> 0.000</td> <td>   -5.887</td> <td>   -5.875</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[T.19]</th>      <td>   -7.0038</td> <td>    0.006</td> <td>-1134.921</td> <td> 0.000</td> <td>   -7.016</td> <td>   -6.992</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[T.24]</th>      <td>   11.4530</td> <td>    0.005</td> <td> 2138.485</td> <td> 0.000</td> <td>   11.442</td> <td>   11.463</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[T.26]</th>      <td>   -4.9926</td> <td>    0.057</td> <td>  -87.248</td> <td> 0.000</td> <td>   -5.105</td> <td>   -4.880</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[T.27]</th>      <td>   -6.6489</td> <td>    0.002</td> <td>-3035.745</td> <td> 0.000</td> <td>   -6.653</td> <td>   -6.645</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[T.32]</th>      <td>    2.8311</td> <td>    0.004</td> <td>  783.293</td> <td> 0.000</td> <td>    2.824</td> <td>    2.838</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[T.33]</th>      <td>   -5.0551</td> <td>    0.003</td> <td>-1976.997</td> <td> 0.000</td> <td>   -5.060</td> <td>   -5.050</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[T.35]</th>      <td>  -12.1648</td> <td>    0.002</td> <td>-6419.425</td> <td> 0.000</td> <td>  -12.168</td> <td>  -12.161</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[T.36]</th>      <td>   -5.0860</td> <td>    0.002</td> <td>-2747.455</td> <td> 0.000</td> <td>   -5.090</td> <td>   -5.082</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[T.41]</th>      <td>   -9.3378</td> <td>    0.002</td> <td>-4259.474</td> <td> 0.000</td> <td>   -9.342</td> <td>   -9.333</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[T.45]</th>      <td>   31.7362</td> <td>    0.004</td> <td> 7777.498</td> <td> 0.000</td> <td>   31.728</td> <td>   31.744</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[T.48]</th>      <td>   -9.3554</td> <td>    0.008</td> <td>-1178.470</td> <td> 0.000</td> <td>   -9.371</td> <td>   -9.340</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[T.50]</th>      <td>    4.5211</td> <td>    0.001</td> <td> 5329.385</td> <td> 0.000</td> <td>    4.519</td> <td>    4.523</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[T.51]</th>      <td>   14.3587</td> <td>    0.005</td> <td> 2944.651</td> <td> 0.000</td> <td>   14.349</td> <td>   14.368</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[T.54]</th>      <td>   -0.9458</td> <td>    0.002</td> <td> -395.092</td> <td> 0.000</td> <td>   -0.951</td> <td>   -0.941</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[T.56]</th>      <td>   -4.0418</td> <td>    0.008</td> <td> -502.618</td> <td> 0.000</td> <td>   -4.058</td> <td>   -4.026</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[T.61]</th>      <td>   -5.9005</td> <td>    0.028</td> <td> -207.214</td> <td> 0.000</td> <td>   -5.956</td> <td>   -5.845</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[T.64]</th>      <td>    4.5639</td> <td>    0.002</td> <td> 2011.492</td> <td> 0.000</td> <td>    4.559</td> <td>    4.568</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[T.77]</th>      <td>    2.9029</td> <td>    0.002</td> <td> 1227.299</td> <td> 0.000</td> <td>    2.898</td> <td>    2.908</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[T.78]</th>      <td>   -0.9554</td> <td>    0.003</td> <td> -360.921</td> <td> 0.000</td> <td>   -0.961</td> <td>   -0.950</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[T.81]</th>      <td>   11.0277</td> <td>    0.015</td> <td>  759.878</td> <td> 0.000</td> <td>   10.999</td> <td>   11.056</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[T.92]</th>      <td>  -12.6547</td> <td>    0.007</td> <td>-1829.850</td> <td> 0.000</td> <td>  -12.668</td> <td>  -12.641</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[T.96]</th>      <td>   12.0367</td> <td>    0.003</td> <td> 4062.390</td> <td> 0.000</td> <td>   12.031</td> <td>   12.043</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[T.97]</th>      <td>  -14.2010</td> <td>    0.003</td> <td>-5278.446</td> <td> 0.000</td> <td>  -14.206</td> <td>  -14.196</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[T.100]</th>     <td>    1.0554</td> <td>    0.006</td> <td>  164.542</td> <td> 0.000</td> <td>    1.043</td> <td>    1.068</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[T.101]</th>     <td>   15.9053</td> <td>    0.002</td> <td> 9047.398</td> <td> 0.000</td> <td>   15.902</td> <td>   15.909</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[T.102]</th>     <td>    5.9764</td> <td>    0.003</td> <td> 2173.020</td> <td> 0.000</td> <td>    5.971</td> <td>    5.982</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_1</th>                  <td>    0.7274</td> <td>    1.131</td> <td>    0.643</td> <td> 0.520</td> <td>   -1.489</td> <td>    2.944</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_2</th>                  <td>    1.0776</td> <td>    1.178</td> <td>    0.915</td> <td> 0.360</td> <td>   -1.231</td> <td>    3.386</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_3</th>                  <td>    1.4320</td> <td>    0.650</td> <td>    2.203</td> <td> 0.028</td> <td>    0.158</td> <td>    2.706</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_4</th>                  <td>    2.3437</td> <td>    0.706</td> <td>    3.319</td> <td> 0.001</td> <td>    0.960</td> <td>    3.728</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_5</th>                  <td>    1.4069</td> <td>    0.763</td> <td>    1.844</td> <td> 0.065</td> <td>   -0.088</td> <td>    2.902</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_7</th>                  <td>    1.7262</td> <td>    0.879</td> <td>    1.964</td> <td> 0.050</td> <td>    0.003</td> <td>    3.449</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_8</th>                  <td>    5.2007</td> <td>    0.988</td> <td>    5.264</td> <td> 0.000</td> <td>    3.264</td> <td>    7.137</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_9</th>                  <td>    3.3093</td> <td>    0.739</td> <td>    4.480</td> <td> 0.000</td> <td>    1.861</td> <td>    4.757</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_10</th>                 <td>    3.2964</td> <td>    0.762</td> <td>    4.325</td> <td> 0.000</td> <td>    1.802</td> <td>    4.790</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_11</th>                 <td>    2.8427</td> <td>    0.564</td> <td>    5.036</td> <td> 0.000</td> <td>    1.736</td> <td>    3.949</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_12</th>                 <td>    1.6255</td> <td>    0.860</td> <td>    1.890</td> <td> 0.059</td> <td>   -0.060</td> <td>    3.312</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_13</th>                 <td>    1.4097</td> <td>    0.479</td> <td>    2.943</td> <td> 0.003</td> <td>    0.471</td> <td>    2.348</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_14</th>                 <td>    2.5709</td> <td>    0.766</td> <td>    3.357</td> <td> 0.001</td> <td>    1.070</td> <td>    4.072</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_15</th>                 <td>    2.0192</td> <td>    0.874</td> <td>    2.310</td> <td> 0.021</td> <td>    0.306</td> <td>    3.733</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_16</th>                 <td>    2.2421</td> <td>    0.647</td> <td>    3.466</td> <td> 0.001</td> <td>    0.974</td> <td>    3.510</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_17</th>                 <td>    2.2467</td> <td>    0.580</td> <td>    3.871</td> <td> 0.000</td> <td>    1.109</td> <td>    3.384</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_18</th>                 <td>    2.8055</td> <td>    0.818</td> <td>    3.429</td> <td> 0.001</td> <td>    1.202</td> <td>    4.409</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_19</th>                 <td>    2.2599</td> <td>    0.837</td> <td>    2.701</td> <td> 0.007</td> <td>    0.620</td> <td>    3.900</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_20</th>                 <td>    2.0988</td> <td>    1.199</td> <td>    1.750</td> <td> 0.080</td> <td>   -0.251</td> <td>    4.449</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_21</th>                 <td>    4.4455</td> <td>    1.680</td> <td>    2.646</td> <td> 0.008</td> <td>    1.153</td> <td>    7.738</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_22</th>                 <td>    3.0887</td> <td>    0.813</td> <td>    3.799</td> <td> 0.000</td> <td>    1.495</td> <td>    4.682</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_23</th>                 <td>    3.0867</td> <td>    0.796</td> <td>    3.877</td> <td> 0.000</td> <td>    1.526</td> <td>    4.647</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_24</th>                 <td>    2.6974</td> <td>    0.822</td> <td>    3.280</td> <td> 0.001</td> <td>    1.086</td> <td>    4.309</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_25</th>                 <td>    3.3204</td> <td>    0.920</td> <td>    3.608</td> <td> 0.000</td> <td>    1.517</td> <td>    5.124</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_26</th>                 <td>    2.2197</td> <td>    0.768</td> <td>    2.889</td> <td> 0.004</td> <td>    0.714</td> <td>    3.725</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_27</th>                 <td>    1.3587</td> <td>    1.341</td> <td>    1.014</td> <td> 0.311</td> <td>   -1.269</td> <td>    3.986</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_28</th>                 <td>    3.5612</td> <td>    0.937</td> <td>    3.801</td> <td> 0.000</td> <td>    1.725</td> <td>    5.398</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_29</th>                 <td>    3.3311</td> <td>    1.236</td> <td>    2.695</td> <td> 0.007</td> <td>    0.909</td> <td>    5.754</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>1839828.070</td> <th>  Durbin-Watson:     </th>   <td>   1.976</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>   <td> 0.000</td>    <th>  Jarque-Bera (JB):  </th> <td>556016762.786</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>            <td> 5.016</td>    <th>  Prob(JB):          </th>   <td>    0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>        <td>91.007</td>    <th>  Cond. No.          </th>   <td>    50.1</td>   \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors are robust to cluster correlation (cluster)"
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}         &     mobility     & \\textbf{  R-squared:         } &       0.038    \\\\\n",
       "\\textbf{Model:}                 &       OLS        & \\textbf{  Adj. R-squared:    } &       0.038    \\\\\n",
       "\\textbf{Method:}                &  Least Squares   & \\textbf{  F-statistic:       } &       1326.    \\\\\n",
       "\\textbf{Date:}                  & Wed, 21 May 2025 & \\textbf{  Prob (F-statistic):} &    7.01e-36    \\\\\n",
       "\\textbf{Time:}                  &     11:46:57     & \\textbf{  Log-Likelihood:    } &  -9.2249e+06   \\\\\n",
       "\\textbf{No. Observations:}      &     1700832      & \\textbf{  AIC:               } &   1.845e+07    \\\\\n",
       "\\textbf{Df Residuals:}          &     1700776      & \\textbf{  BIC:               } &   1.845e+07    \\\\\n",
       "\\textbf{Df Model:}              &          55      & \\textbf{                     } &                \\\\\n",
       "\\textbf{Covariance Type:}       &     cluster      & \\textbf{                     } &                \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                                & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{time\\_of\\_day[evening]} &      27.5899  &        0.458     &    60.215  &         0.000        &       26.692    &       28.488     \\\\\n",
       "\\textbf{C(strike)[T.17]}        &      -5.8809  &        0.003     & -1917.523  &         0.000        &       -5.887    &       -5.875     \\\\\n",
       "\\textbf{C(strike)[T.19]}        &      -7.0038  &        0.006     & -1134.921  &         0.000        &       -7.016    &       -6.992     \\\\\n",
       "\\textbf{C(strike)[T.24]}        &      11.4530  &        0.005     &  2138.485  &         0.000        &       11.442    &       11.463     \\\\\n",
       "\\textbf{C(strike)[T.26]}        &      -4.9926  &        0.057     &   -87.248  &         0.000        &       -5.105    &       -4.880     \\\\\n",
       "\\textbf{C(strike)[T.27]}        &      -6.6489  &        0.002     & -3035.745  &         0.000        &       -6.653    &       -6.645     \\\\\n",
       "\\textbf{C(strike)[T.32]}        &       2.8311  &        0.004     &   783.293  &         0.000        &        2.824    &        2.838     \\\\\n",
       "\\textbf{C(strike)[T.33]}        &      -5.0551  &        0.003     & -1976.997  &         0.000        &       -5.060    &       -5.050     \\\\\n",
       "\\textbf{C(strike)[T.35]}        &     -12.1648  &        0.002     & -6419.425  &         0.000        &      -12.168    &      -12.161     \\\\\n",
       "\\textbf{C(strike)[T.36]}        &      -5.0860  &        0.002     & -2747.455  &         0.000        &       -5.090    &       -5.082     \\\\\n",
       "\\textbf{C(strike)[T.41]}        &      -9.3378  &        0.002     & -4259.474  &         0.000        &       -9.342    &       -9.333     \\\\\n",
       "\\textbf{C(strike)[T.45]}        &      31.7362  &        0.004     &  7777.498  &         0.000        &       31.728    &       31.744     \\\\\n",
       "\\textbf{C(strike)[T.48]}        &      -9.3554  &        0.008     & -1178.470  &         0.000        &       -9.371    &       -9.340     \\\\\n",
       "\\textbf{C(strike)[T.50]}        &       4.5211  &        0.001     &  5329.385  &         0.000        &        4.519    &        4.523     \\\\\n",
       "\\textbf{C(strike)[T.51]}        &      14.3587  &        0.005     &  2944.651  &         0.000        &       14.349    &       14.368     \\\\\n",
       "\\textbf{C(strike)[T.54]}        &      -0.9458  &        0.002     &  -395.092  &         0.000        &       -0.951    &       -0.941     \\\\\n",
       "\\textbf{C(strike)[T.56]}        &      -4.0418  &        0.008     &  -502.618  &         0.000        &       -4.058    &       -4.026     \\\\\n",
       "\\textbf{C(strike)[T.61]}        &      -5.9005  &        0.028     &  -207.214  &         0.000        &       -5.956    &       -5.845     \\\\\n",
       "\\textbf{C(strike)[T.64]}        &       4.5639  &        0.002     &  2011.492  &         0.000        &        4.559    &        4.568     \\\\\n",
       "\\textbf{C(strike)[T.77]}        &       2.9029  &        0.002     &  1227.299  &         0.000        &        2.898    &        2.908     \\\\\n",
       "\\textbf{C(strike)[T.78]}        &      -0.9554  &        0.003     &  -360.921  &         0.000        &       -0.961    &       -0.950     \\\\\n",
       "\\textbf{C(strike)[T.81]}        &      11.0277  &        0.015     &   759.878  &         0.000        &       10.999    &       11.056     \\\\\n",
       "\\textbf{C(strike)[T.92]}        &     -12.6547  &        0.007     & -1829.850  &         0.000        &      -12.668    &      -12.641     \\\\\n",
       "\\textbf{C(strike)[T.96]}        &      12.0367  &        0.003     &  4062.390  &         0.000        &       12.031    &       12.043     \\\\\n",
       "\\textbf{C(strike)[T.97]}        &     -14.2010  &        0.003     & -5278.446  &         0.000        &      -14.206    &      -14.196     \\\\\n",
       "\\textbf{C(strike)[T.100]}       &       1.0554  &        0.006     &   164.542  &         0.000        &        1.043    &        1.068     \\\\\n",
       "\\textbf{C(strike)[T.101]}       &      15.9053  &        0.002     &  9047.398  &         0.000        &       15.902    &       15.909     \\\\\n",
       "\\textbf{C(strike)[T.102]}       &       5.9764  &        0.003     &  2173.020  &         0.000        &        5.971    &        5.982     \\\\\n",
       "\\textbf{X\\_1}                   &       0.7274  &        1.131     &     0.643  &         0.520        &       -1.489    &        2.944     \\\\\n",
       "\\textbf{X\\_2}                   &       1.0776  &        1.178     &     0.915  &         0.360        &       -1.231    &        3.386     \\\\\n",
       "\\textbf{X\\_3}                   &       1.4320  &        0.650     &     2.203  &         0.028        &        0.158    &        2.706     \\\\\n",
       "\\textbf{X\\_4}                   &       2.3437  &        0.706     &     3.319  &         0.001        &        0.960    &        3.728     \\\\\n",
       "\\textbf{X\\_5}                   &       1.4069  &        0.763     &     1.844  &         0.065        &       -0.088    &        2.902     \\\\\n",
       "\\textbf{X\\_7}                   &       1.7262  &        0.879     &     1.964  &         0.050        &        0.003    &        3.449     \\\\\n",
       "\\textbf{X\\_8}                   &       5.2007  &        0.988     &     5.264  &         0.000        &        3.264    &        7.137     \\\\\n",
       "\\textbf{X\\_9}                   &       3.3093  &        0.739     &     4.480  &         0.000        &        1.861    &        4.757     \\\\\n",
       "\\textbf{X\\_10}                  &       3.2964  &        0.762     &     4.325  &         0.000        &        1.802    &        4.790     \\\\\n",
       "\\textbf{X\\_11}                  &       2.8427  &        0.564     &     5.036  &         0.000        &        1.736    &        3.949     \\\\\n",
       "\\textbf{X\\_12}                  &       1.6255  &        0.860     &     1.890  &         0.059        &       -0.060    &        3.312     \\\\\n",
       "\\textbf{X\\_13}                  &       1.4097  &        0.479     &     2.943  &         0.003        &        0.471    &        2.348     \\\\\n",
       "\\textbf{X\\_14}                  &       2.5709  &        0.766     &     3.357  &         0.001        &        1.070    &        4.072     \\\\\n",
       "\\textbf{X\\_15}                  &       2.0192  &        0.874     &     2.310  &         0.021        &        0.306    &        3.733     \\\\\n",
       "\\textbf{X\\_16}                  &       2.2421  &        0.647     &     3.466  &         0.001        &        0.974    &        3.510     \\\\\n",
       "\\textbf{X\\_17}                  &       2.2467  &        0.580     &     3.871  &         0.000        &        1.109    &        3.384     \\\\\n",
       "\\textbf{X\\_18}                  &       2.8055  &        0.818     &     3.429  &         0.001        &        1.202    &        4.409     \\\\\n",
       "\\textbf{X\\_19}                  &       2.2599  &        0.837     &     2.701  &         0.007        &        0.620    &        3.900     \\\\\n",
       "\\textbf{X\\_20}                  &       2.0988  &        1.199     &     1.750  &         0.080        &       -0.251    &        4.449     \\\\\n",
       "\\textbf{X\\_21}                  &       4.4455  &        1.680     &     2.646  &         0.008        &        1.153    &        7.738     \\\\\n",
       "\\textbf{X\\_22}                  &       3.0887  &        0.813     &     3.799  &         0.000        &        1.495    &        4.682     \\\\\n",
       "\\textbf{X\\_23}                  &       3.0867  &        0.796     &     3.877  &         0.000        &        1.526    &        4.647     \\\\\n",
       "\\textbf{X\\_24}                  &       2.6974  &        0.822     &     3.280  &         0.001        &        1.086    &        4.309     \\\\\n",
       "\\textbf{X\\_25}                  &       3.3204  &        0.920     &     3.608  &         0.000        &        1.517    &        5.124     \\\\\n",
       "\\textbf{X\\_26}                  &       2.2197  &        0.768     &     2.889  &         0.004        &        0.714    &        3.725     \\\\\n",
       "\\textbf{X\\_27}                  &       1.3587  &        1.341     &     1.014  &         0.311        &       -1.269    &        3.986     \\\\\n",
       "\\textbf{X\\_28}                  &       3.5612  &        0.937     &     3.801  &         0.000        &        1.725    &        5.398     \\\\\n",
       "\\textbf{X\\_29}                  &       3.3311  &        1.236     &     2.695  &         0.007        &        0.909    &        5.754     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 1839828.070 & \\textbf{  Durbin-Watson:     } &       1.976    \\\\\n",
       "\\textbf{Prob(Omnibus):} &     0.000   & \\textbf{  Jarque-Bera (JB):  } & 556016762.786  \\\\\n",
       "\\textbf{Skew:}          &     5.016   & \\textbf{  Prob(JB):          } &        0.00    \\\\\n",
       "\\textbf{Kurtosis:}      &    91.007   & \\textbf{  Cond. No.          } &        50.1    \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors are robust to cluster correlation (cluster)"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:               mobility   R-squared:                       0.038\n",
       "Model:                            OLS   Adj. R-squared:                  0.038\n",
       "Method:                 Least Squares   F-statistic:                     1326.\n",
       "Date:                Wed, 21 May 2025   Prob (F-statistic):           7.01e-36\n",
       "Time:                        11:46:57   Log-Likelihood:            -9.2249e+06\n",
       "No. Observations:             1700832   AIC:                         1.845e+07\n",
       "Df Residuals:                 1700776   BIC:                         1.845e+07\n",
       "Df Model:                          55                                         \n",
       "Covariance Type:              cluster                                         \n",
       "========================================================================================\n",
       "                           coef    std err          z      P>|z|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------------\n",
       "time_of_day[evening]    27.5899      0.458     60.215      0.000      26.692      28.488\n",
       "C(strike)[T.17]         -5.8809      0.003  -1917.523      0.000      -5.887      -5.875\n",
       "C(strike)[T.19]         -7.0038      0.006  -1134.921      0.000      -7.016      -6.992\n",
       "C(strike)[T.24]         11.4530      0.005   2138.485      0.000      11.442      11.463\n",
       "C(strike)[T.26]         -4.9926      0.057    -87.248      0.000      -5.105      -4.880\n",
       "C(strike)[T.27]         -6.6489      0.002  -3035.745      0.000      -6.653      -6.645\n",
       "C(strike)[T.32]          2.8311      0.004    783.293      0.000       2.824       2.838\n",
       "C(strike)[T.33]         -5.0551      0.003  -1976.997      0.000      -5.060      -5.050\n",
       "C(strike)[T.35]        -12.1648      0.002  -6419.425      0.000     -12.168     -12.161\n",
       "C(strike)[T.36]         -5.0860      0.002  -2747.455      0.000      -5.090      -5.082\n",
       "C(strike)[T.41]         -9.3378      0.002  -4259.474      0.000      -9.342      -9.333\n",
       "C(strike)[T.45]         31.7362      0.004   7777.498      0.000      31.728      31.744\n",
       "C(strike)[T.48]         -9.3554      0.008  -1178.470      0.000      -9.371      -9.340\n",
       "C(strike)[T.50]          4.5211      0.001   5329.385      0.000       4.519       4.523\n",
       "C(strike)[T.51]         14.3587      0.005   2944.651      0.000      14.349      14.368\n",
       "C(strike)[T.54]         -0.9458      0.002   -395.092      0.000      -0.951      -0.941\n",
       "C(strike)[T.56]         -4.0418      0.008   -502.618      0.000      -4.058      -4.026\n",
       "C(strike)[T.61]         -5.9005      0.028   -207.214      0.000      -5.956      -5.845\n",
       "C(strike)[T.64]          4.5639      0.002   2011.492      0.000       4.559       4.568\n",
       "C(strike)[T.77]          2.9029      0.002   1227.299      0.000       2.898       2.908\n",
       "C(strike)[T.78]         -0.9554      0.003   -360.921      0.000      -0.961      -0.950\n",
       "C(strike)[T.81]         11.0277      0.015    759.878      0.000      10.999      11.056\n",
       "C(strike)[T.92]        -12.6547      0.007  -1829.850      0.000     -12.668     -12.641\n",
       "C(strike)[T.96]         12.0367      0.003   4062.390      0.000      12.031      12.043\n",
       "C(strike)[T.97]        -14.2010      0.003  -5278.446      0.000     -14.206     -14.196\n",
       "C(strike)[T.100]         1.0554      0.006    164.542      0.000       1.043       1.068\n",
       "C(strike)[T.101]        15.9053      0.002   9047.398      0.000      15.902      15.909\n",
       "C(strike)[T.102]         5.9764      0.003   2173.020      0.000       5.971       5.982\n",
       "X_1                      0.7274      1.131      0.643      0.520      -1.489       2.944\n",
       "X_2                      1.0776      1.178      0.915      0.360      -1.231       3.386\n",
       "X_3                      1.4320      0.650      2.203      0.028       0.158       2.706\n",
       "X_4                      2.3437      0.706      3.319      0.001       0.960       3.728\n",
       "X_5                      1.4069      0.763      1.844      0.065      -0.088       2.902\n",
       "X_7                      1.7262      0.879      1.964      0.050       0.003       3.449\n",
       "X_8                      5.2007      0.988      5.264      0.000       3.264       7.137\n",
       "X_9                      3.3093      0.739      4.480      0.000       1.861       4.757\n",
       "X_10                     3.2964      0.762      4.325      0.000       1.802       4.790\n",
       "X_11                     2.8427      0.564      5.036      0.000       1.736       3.949\n",
       "X_12                     1.6255      0.860      1.890      0.059      -0.060       3.312\n",
       "X_13                     1.4097      0.479      2.943      0.003       0.471       2.348\n",
       "X_14                     2.5709      0.766      3.357      0.001       1.070       4.072\n",
       "X_15                     2.0192      0.874      2.310      0.021       0.306       3.733\n",
       "X_16                     2.2421      0.647      3.466      0.001       0.974       3.510\n",
       "X_17                     2.2467      0.580      3.871      0.000       1.109       3.384\n",
       "X_18                     2.8055      0.818      3.429      0.001       1.202       4.409\n",
       "X_19                     2.2599      0.837      2.701      0.007       0.620       3.900\n",
       "X_20                     2.0988      1.199      1.750      0.080      -0.251       4.449\n",
       "X_21                     4.4455      1.680      2.646      0.008       1.153       7.738\n",
       "X_22                     3.0887      0.813      3.799      0.000       1.495       4.682\n",
       "X_23                     3.0867      0.796      3.877      0.000       1.526       4.647\n",
       "X_24                     2.6974      0.822      3.280      0.001       1.086       4.309\n",
       "X_25                     3.3204      0.920      3.608      0.000       1.517       5.124\n",
       "X_26                     2.2197      0.768      2.889      0.004       0.714       3.725\n",
       "X_27                     1.3587      1.341      1.014      0.311      -1.269       3.986\n",
       "X_28                     3.5612      0.937      3.801      0.000       1.725       5.398\n",
       "X_29                     3.3311      1.236      2.695      0.007       0.909       5.754\n",
       "==============================================================================\n",
       "Omnibus:                  1839828.070   Durbin-Watson:                   1.976\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):        556016762.786\n",
       "Skew:                           5.016   Prob(JB):                         0.00\n",
       "Kurtosis:                      91.007   Cond. No.                         50.1\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors are robust to cluster correlation (cluster)\n",
       "\"\"\""
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# run the event study regression for mobility around strikes\n",
    "# 7 lags and 21 leads\n",
    "# time vars provide the parameters of interest\n",
    "# drop the day = -2 variable for difference in means and to avoid multicollinearity\n",
    "# use strike fixed effects and cluster SE by strike\n",
    "\n",
    "reg = sm.ols('mobility ~ ' + var_form + ' +  C(strike) - 1 ', \n",
    "             data=df_evening).fit(cov_type='cluster',\n",
    "                          cov_kwds={'groups': df_evening['strike'].values})\n",
    "\n",
    "# save parameters and confidence intervals for plotting\n",
    "conf_int = reg.conf_int()\n",
    "res_evening = pd.DataFrame({\n",
    "    'params': reg.params,\n",
    "    'ci_l': conf_int[0],  # lower bound\n",
    "    'ci_h': conf_int[1],  # upper bound\n",
    "})\n",
    "\n",
    "# print regression summary\n",
    "reg.summary()\n",
    "# reindex the results to go from day = -7 to day = 21\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_three_group(event_res1, event_res2, event_res3, label1='Group 1', label2='Group 2', label3='Group 3',\n",
    "                       color1='C0', color2='C1', color3='C2', ylabel='Distance (km)', ylim=[-7,21], xlim=[-7,21],\n",
    "                       outfile='figures/figureXX.pdf'):\n",
    "\n",
    "    plt.figure()\n",
    "    ax = plt.gca()\n",
    "\n",
    "    # Plot for first group\n",
    "    ax.plot(event_res1['day'], event_res1['params'], color=color1, label=label1, zorder=3)\n",
    "    ax.fill_between(event_res1['day'], event_res1['ci_l'], event_res1['ci_h'],\n",
    "                    facecolor=color1, alpha=0.2, zorder=2)\n",
    "\n",
    "    # Plot for second group\n",
    "    ax.plot(event_res2['day'], event_res2['params'], color=color2, label=label2, zorder=3)\n",
    "    ax.fill_between(event_res2['day'], event_res2['ci_l'], event_res2['ci_h'],\n",
    "                    facecolor=color2, alpha=0.2, zorder=2)\n",
    "    \n",
    "    # Plot for third group\n",
    "    ax.plot(event_res3['day'], event_res3['params'], color=color3, label=label3, zorder=3)\n",
    "    ax.fill_between(event_res3['day'], event_res3['ci_l'], event_res3['ci_h'],\n",
    "                    facecolor=color3, alpha=0.2, zorder=2)\n",
    "\n",
    "    # Reference lines\n",
    "    ax.axhline(y=0, color='k', alpha=0.7, linestyle='--', zorder=1)\n",
    "    ax.axvline(x=0, color='k', alpha=0.3, linestyle='--', zorder=1)\n",
    "\n",
    "    # Labels and limits\n",
    "    ax.set_xlim(xlim)\n",
    "    ax.set_ylim(ylim)\n",
    "    ax.set_ylabel(ylabel)\n",
    "    ax.set_xlabel('Days since strike')\n",
    "    ax.legend()\n",
    "\n",
    "    # Save\n",
    "    fig = ax.get_figure()\n",
    "    fig.set_size_inches(10, 6)\n",
    "    fig.savefig(outfile, bbox_inches='tight', format='pdf', dpi=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "event_res_morning = res_morning[res_morning.index.to_series().str.startswith(\"X_\")].copy()\n",
    "event_res_morning = event_res_morning.reset_index().rename(columns={'index': 'varname'})\n",
    "event_res_morning['day'] = event_res_morning['varname'].str.extract(r'X_(\\d+)').astype(float).astype('Int64') - 8\n",
    "\n",
    "event_res_day = res_day[res_day.index.to_series().str.startswith(\"X_\")].copy()\n",
    "event_res_day = event_res_day.reset_index().rename(columns={'index': 'varname'})\n",
    "event_res_day['day'] = event_res_day['varname'].str.extract(r'X_(\\d+)').astype(float).astype('Int64') - 8\n",
    "\n",
    "event_res_evening = res_evening[res_evening.index.to_series().str.startswith(\"X_\")].copy()\n",
    "event_res_evening = event_res_evening.reset_index().rename(columns={'index': 'varname'})\n",
    "event_res_evening['day'] = event_res_evening['varname'].str.extract(r'X_(\\d+)').astype(float).astype('Int64') - 8\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_three_group(event_res_morning, event_res_day, event_res_evening,\n",
    "                       label1='Morning (0:00 - 8:00)', label2='Day (8:01 - 16:00)', label3='Evening (16:01 - 23:59)',\n",
    "                       color1='C0', color2='C1', color3='C2',\n",
    "                       ylabel='Distance (km)',\n",
    "                       outfile='figures/figureX_time_of_day.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      74\n",
       "unique      3\n",
       "top       day\n",
       "freq       28\n",
       "Name: time_of_day, dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strikes['time_of_day'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Results: Strikes that occur later in the day are associated with larger mobility; and the effect is quite stark. It's much larger than the association with population density, but smaller than the effect associated with kiling a high-ranking militant"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure S12A: Mobility Results with High-Ranking Militant Subgroups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create separate dataframes for strikes that kill high ranking militants or not\n",
    "df_high_ranking = df[df['high_ranking'] == 1]\n",
    "df_low_ranking = df[df['high_ranking'] == 0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/miniconda3/envs/drones/lib/python3.12/site-packages/statsmodels/base/model.py:1894: ValueWarning: covariance of constraints does not have full rank. The number of constraints is 46, but rank is 16\n",
      "  warnings.warn('covariance of constraints does not have full '\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>mobility</td>     <th>  R-squared:         </th>  <td>   0.043</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.043</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>-8.007e+09</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 21 May 2025</td> <th>  Prob (F-statistic):</th>   <td>  1.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>12:22:26</td>     <th>  Log-Likelihood:    </th> <td>-6.3699e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>1181594</td>     <th>  AIC:               </th>  <td>1.274e+07</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>1181549</td>     <th>  BIC:               </th>  <td>1.274e+07</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    44</td>      <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>       <td>cluster</td>     <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "          <td></td>             <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[8]</th>      <td>    2.8031</td> <td>    0.059</td> <td>   47.693</td> <td> 0.000</td> <td>    2.688</td> <td>    2.918</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[9]</th>      <td>   -7.3007</td> <td>    0.024</td> <td> -307.056</td> <td> 0.000</td> <td>   -7.347</td> <td>   -7.254</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[27]</th>     <td>   -6.8232</td> <td>    0.034</td> <td> -199.831</td> <td> 0.000</td> <td>   -6.890</td> <td>   -6.756</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[32]</th>     <td>    5.5182</td> <td>    0.017</td> <td>  315.502</td> <td> 0.000</td> <td>    5.484</td> <td>    5.552</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[36]</th>     <td>   -0.6520</td> <td>    0.053</td> <td>  -12.339</td> <td> 0.000</td> <td>   -0.756</td> <td>   -0.548</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[42]</th>     <td>   -5.8294</td> <td>    0.061</td> <td>  -95.201</td> <td> 0.000</td> <td>   -5.949</td> <td>   -5.709</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[51]</th>     <td>   18.7958</td> <td>    0.050</td> <td>  379.603</td> <td> 0.000</td> <td>   18.699</td> <td>   18.893</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[56]</th>     <td>   -1.0318</td> <td>    0.021</td> <td>  -49.073</td> <td> 0.000</td> <td>   -1.073</td> <td>   -0.991</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[59]</th>     <td>   -5.2600</td> <td>    0.058</td> <td>  -90.001</td> <td> 0.000</td> <td>   -5.375</td> <td>   -5.145</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[60]</th>     <td>    4.1407</td> <td>    0.020</td> <td>  204.181</td> <td> 0.000</td> <td>    4.101</td> <td>    4.180</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[68]</th>     <td>   -0.6031</td> <td>    0.059</td> <td>  -10.145</td> <td> 0.000</td> <td>   -0.720</td> <td>   -0.487</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[70]</th>     <td>    0.2062</td> <td>    0.059</td> <td>    3.523</td> <td> 0.000</td> <td>    0.091</td> <td>    0.321</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[75]</th>     <td>   15.9319</td> <td>    0.054</td> <td>  294.240</td> <td> 0.000</td> <td>   15.826</td> <td>   16.038</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[90]</th>     <td>   -8.8401</td> <td>    0.055</td> <td> -160.488</td> <td> 0.000</td> <td>   -8.948</td> <td>   -8.732</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[92]</th>     <td>   -8.3738</td> <td>    0.045</td> <td> -185.891</td> <td> 0.000</td> <td>   -8.462</td> <td>   -8.286</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[96]</th>     <td>   15.9897</td> <td>    0.048</td> <td>  333.037</td> <td> 0.000</td> <td>   15.896</td> <td>   16.084</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[100]</th>    <td>    4.8587</td> <td>    0.037</td> <td>  132.407</td> <td> 0.000</td> <td>    4.787</td> <td>    4.931</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_1</th>               <td>   -0.8290</td> <td>    0.731</td> <td>   -1.134</td> <td> 0.257</td> <td>   -2.261</td> <td>    0.603</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_2</th>               <td>   -1.4974</td> <td>    0.743</td> <td>   -2.016</td> <td> 0.044</td> <td>   -2.953</td> <td>   -0.042</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_3</th>               <td>    1.0697</td> <td>    0.596</td> <td>    1.794</td> <td> 0.073</td> <td>   -0.099</td> <td>    2.238</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_4</th>               <td>    0.5512</td> <td>    0.491</td> <td>    1.123</td> <td> 0.261</td> <td>   -0.411</td> <td>    1.513</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_5</th>               <td>   -0.7341</td> <td>    0.259</td> <td>   -2.830</td> <td> 0.005</td> <td>   -1.242</td> <td>   -0.226</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_7</th>               <td>    0.0718</td> <td>    0.308</td> <td>    0.233</td> <td> 0.816</td> <td>   -0.532</td> <td>    0.676</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_8</th>               <td>    4.6943</td> <td>    1.192</td> <td>    3.938</td> <td> 0.000</td> <td>    2.358</td> <td>    7.031</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_9</th>               <td>    1.7411</td> <td>    0.633</td> <td>    2.752</td> <td> 0.006</td> <td>    0.501</td> <td>    2.981</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_10</th>              <td>    1.2915</td> <td>    0.492</td> <td>    2.624</td> <td> 0.009</td> <td>    0.327</td> <td>    2.256</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_11</th>              <td>    1.3943</td> <td>    0.480</td> <td>    2.907</td> <td> 0.004</td> <td>    0.454</td> <td>    2.334</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_12</th>              <td>   -1.3401</td> <td>    0.472</td> <td>   -2.841</td> <td> 0.004</td> <td>   -2.264</td> <td>   -0.416</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_13</th>              <td>    0.9406</td> <td>    0.554</td> <td>    1.698</td> <td> 0.090</td> <td>   -0.145</td> <td>    2.026</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_14</th>              <td>    0.6745</td> <td>    0.592</td> <td>    1.139</td> <td> 0.255</td> <td>   -0.486</td> <td>    1.835</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_15</th>              <td>    0.9479</td> <td>    0.581</td> <td>    1.630</td> <td> 0.103</td> <td>   -0.192</td> <td>    2.087</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_16</th>              <td>    1.7111</td> <td>    0.709</td> <td>    2.412</td> <td> 0.016</td> <td>    0.321</td> <td>    3.101</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_17</th>              <td>    1.5232</td> <td>    0.740</td> <td>    2.058</td> <td> 0.040</td> <td>    0.072</td> <td>    2.974</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_18</th>              <td>    2.0074</td> <td>    1.362</td> <td>    1.474</td> <td> 0.140</td> <td>   -0.661</td> <td>    4.676</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_19</th>              <td>    0.7787</td> <td>    1.542</td> <td>    0.505</td> <td> 0.613</td> <td>   -2.243</td> <td>    3.800</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_20</th>              <td>    2.4656</td> <td>    1.570</td> <td>    1.571</td> <td> 0.116</td> <td>   -0.611</td> <td>    5.542</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_21</th>              <td>    4.3969</td> <td>    2.489</td> <td>    1.766</td> <td> 0.077</td> <td>   -0.482</td> <td>    9.276</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_22</th>              <td>    2.2369</td> <td>    1.032</td> <td>    2.168</td> <td> 0.030</td> <td>    0.214</td> <td>    4.259</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_23</th>              <td>    2.2113</td> <td>    1.303</td> <td>    1.697</td> <td> 0.090</td> <td>   -0.343</td> <td>    4.766</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_24</th>              <td>    2.9298</td> <td>    0.902</td> <td>    3.249</td> <td> 0.001</td> <td>    1.162</td> <td>    4.697</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_25</th>              <td>    2.0018</td> <td>    1.405</td> <td>    1.425</td> <td> 0.154</td> <td>   -0.752</td> <td>    4.755</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_26</th>              <td>    0.4972</td> <td>    1.086</td> <td>    0.458</td> <td> 0.647</td> <td>   -1.632</td> <td>    2.626</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_27</th>              <td>    2.9825</td> <td>    1.242</td> <td>    2.401</td> <td> 0.016</td> <td>    0.548</td> <td>    5.417</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_28</th>              <td>    2.4846</td> <td>    1.100</td> <td>    2.260</td> <td> 0.024</td> <td>    0.329</td> <td>    4.640</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_29</th>              <td>    3.4875</td> <td>    1.561</td> <td>    2.234</td> <td> 0.025</td> <td>    0.428</td> <td>    6.547</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>total_killed_high</th> <td>    0.1589</td> <td>    0.003</td> <td>   52.746</td> <td> 0.000</td> <td>    0.153</td> <td>    0.165</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>high_ranking</th>      <td>   23.5302</td> <td>    0.566</td> <td>   41.586</td> <td> 0.000</td> <td>   22.421</td> <td>   24.639</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>1371665.459</td> <th>  Durbin-Watson:     </th>   <td>   1.972</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>   <td> 0.000</td>    <th>  Jarque-Bera (JB):  </th> <td>645989751.340</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>            <td> 5.567</td>    <th>  Prob(JB):          </th>   <td>    0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>        <td>117.005</td>   <th>  Cond. No.          </th>   <td>3.43e+15</td>   \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors are robust to cluster correlation (cluster)<br/>[2] The smallest eigenvalue is 1.02e-23. This might indicate that there are<br/>strong multicollinearity problems or that the design matrix is singular."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}      &     mobility     & \\textbf{  R-squared:         } &       0.043    \\\\\n",
       "\\textbf{Model:}              &       OLS        & \\textbf{  Adj. R-squared:    } &       0.043    \\\\\n",
       "\\textbf{Method:}             &  Least Squares   & \\textbf{  F-statistic:       } &   -8.007e+09   \\\\\n",
       "\\textbf{Date:}               & Wed, 21 May 2025 & \\textbf{  Prob (F-statistic):} &       1.00     \\\\\n",
       "\\textbf{Time:}               &     12:22:26     & \\textbf{  Log-Likelihood:    } &  -6.3699e+06   \\\\\n",
       "\\textbf{No. Observations:}   &     1181594      & \\textbf{  AIC:               } &   1.274e+07    \\\\\n",
       "\\textbf{Df Residuals:}       &     1181549      & \\textbf{  BIC:               } &   1.274e+07    \\\\\n",
       "\\textbf{Df Model:}           &          44      & \\textbf{                     } &                \\\\\n",
       "\\textbf{Covariance Type:}    &     cluster      & \\textbf{                     } &                \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                             & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{C(strike)[8]}        &       2.8031  &        0.059     &    47.693  &         0.000        &        2.688    &        2.918     \\\\\n",
       "\\textbf{C(strike)[9]}        &      -7.3007  &        0.024     &  -307.056  &         0.000        &       -7.347    &       -7.254     \\\\\n",
       "\\textbf{C(strike)[27]}       &      -6.8232  &        0.034     &  -199.831  &         0.000        &       -6.890    &       -6.756     \\\\\n",
       "\\textbf{C(strike)[32]}       &       5.5182  &        0.017     &   315.502  &         0.000        &        5.484    &        5.552     \\\\\n",
       "\\textbf{C(strike)[36]}       &      -0.6520  &        0.053     &   -12.339  &         0.000        &       -0.756    &       -0.548     \\\\\n",
       "\\textbf{C(strike)[42]}       &      -5.8294  &        0.061     &   -95.201  &         0.000        &       -5.949    &       -5.709     \\\\\n",
       "\\textbf{C(strike)[51]}       &      18.7958  &        0.050     &   379.603  &         0.000        &       18.699    &       18.893     \\\\\n",
       "\\textbf{C(strike)[56]}       &      -1.0318  &        0.021     &   -49.073  &         0.000        &       -1.073    &       -0.991     \\\\\n",
       "\\textbf{C(strike)[59]}       &      -5.2600  &        0.058     &   -90.001  &         0.000        &       -5.375    &       -5.145     \\\\\n",
       "\\textbf{C(strike)[60]}       &       4.1407  &        0.020     &   204.181  &         0.000        &        4.101    &        4.180     \\\\\n",
       "\\textbf{C(strike)[68]}       &      -0.6031  &        0.059     &   -10.145  &         0.000        &       -0.720    &       -0.487     \\\\\n",
       "\\textbf{C(strike)[70]}       &       0.2062  &        0.059     &     3.523  &         0.000        &        0.091    &        0.321     \\\\\n",
       "\\textbf{C(strike)[75]}       &      15.9319  &        0.054     &   294.240  &         0.000        &       15.826    &       16.038     \\\\\n",
       "\\textbf{C(strike)[90]}       &      -8.8401  &        0.055     &  -160.488  &         0.000        &       -8.948    &       -8.732     \\\\\n",
       "\\textbf{C(strike)[92]}       &      -8.3738  &        0.045     &  -185.891  &         0.000        &       -8.462    &       -8.286     \\\\\n",
       "\\textbf{C(strike)[96]}       &      15.9897  &        0.048     &   333.037  &         0.000        &       15.896    &       16.084     \\\\\n",
       "\\textbf{C(strike)[100]}      &       4.8587  &        0.037     &   132.407  &         0.000        &        4.787    &        4.931     \\\\\n",
       "\\textbf{X\\_1}                &      -0.8290  &        0.731     &    -1.134  &         0.257        &       -2.261    &        0.603     \\\\\n",
       "\\textbf{X\\_2}                &      -1.4974  &        0.743     &    -2.016  &         0.044        &       -2.953    &       -0.042     \\\\\n",
       "\\textbf{X\\_3}                &       1.0697  &        0.596     &     1.794  &         0.073        &       -0.099    &        2.238     \\\\\n",
       "\\textbf{X\\_4}                &       0.5512  &        0.491     &     1.123  &         0.261        &       -0.411    &        1.513     \\\\\n",
       "\\textbf{X\\_5}                &      -0.7341  &        0.259     &    -2.830  &         0.005        &       -1.242    &       -0.226     \\\\\n",
       "\\textbf{X\\_7}                &       0.0718  &        0.308     &     0.233  &         0.816        &       -0.532    &        0.676     \\\\\n",
       "\\textbf{X\\_8}                &       4.6943  &        1.192     &     3.938  &         0.000        &        2.358    &        7.031     \\\\\n",
       "\\textbf{X\\_9}                &       1.7411  &        0.633     &     2.752  &         0.006        &        0.501    &        2.981     \\\\\n",
       "\\textbf{X\\_10}               &       1.2915  &        0.492     &     2.624  &         0.009        &        0.327    &        2.256     \\\\\n",
       "\\textbf{X\\_11}               &       1.3943  &        0.480     &     2.907  &         0.004        &        0.454    &        2.334     \\\\\n",
       "\\textbf{X\\_12}               &      -1.3401  &        0.472     &    -2.841  &         0.004        &       -2.264    &       -0.416     \\\\\n",
       "\\textbf{X\\_13}               &       0.9406  &        0.554     &     1.698  &         0.090        &       -0.145    &        2.026     \\\\\n",
       "\\textbf{X\\_14}               &       0.6745  &        0.592     &     1.139  &         0.255        &       -0.486    &        1.835     \\\\\n",
       "\\textbf{X\\_15}               &       0.9479  &        0.581     &     1.630  &         0.103        &       -0.192    &        2.087     \\\\\n",
       "\\textbf{X\\_16}               &       1.7111  &        0.709     &     2.412  &         0.016        &        0.321    &        3.101     \\\\\n",
       "\\textbf{X\\_17}               &       1.5232  &        0.740     &     2.058  &         0.040        &        0.072    &        2.974     \\\\\n",
       "\\textbf{X\\_18}               &       2.0074  &        1.362     &     1.474  &         0.140        &       -0.661    &        4.676     \\\\\n",
       "\\textbf{X\\_19}               &       0.7787  &        1.542     &     0.505  &         0.613        &       -2.243    &        3.800     \\\\\n",
       "\\textbf{X\\_20}               &       2.4656  &        1.570     &     1.571  &         0.116        &       -0.611    &        5.542     \\\\\n",
       "\\textbf{X\\_21}               &       4.3969  &        2.489     &     1.766  &         0.077        &       -0.482    &        9.276     \\\\\n",
       "\\textbf{X\\_22}               &       2.2369  &        1.032     &     2.168  &         0.030        &        0.214    &        4.259     \\\\\n",
       "\\textbf{X\\_23}               &       2.2113  &        1.303     &     1.697  &         0.090        &       -0.343    &        4.766     \\\\\n",
       "\\textbf{X\\_24}               &       2.9298  &        0.902     &     3.249  &         0.001        &        1.162    &        4.697     \\\\\n",
       "\\textbf{X\\_25}               &       2.0018  &        1.405     &     1.425  &         0.154        &       -0.752    &        4.755     \\\\\n",
       "\\textbf{X\\_26}               &       0.4972  &        1.086     &     0.458  &         0.647        &       -1.632    &        2.626     \\\\\n",
       "\\textbf{X\\_27}               &       2.9825  &        1.242     &     2.401  &         0.016        &        0.548    &        5.417     \\\\\n",
       "\\textbf{X\\_28}               &       2.4846  &        1.100     &     2.260  &         0.024        &        0.329    &        4.640     \\\\\n",
       "\\textbf{X\\_29}               &       3.4875  &        1.561     &     2.234  &         0.025        &        0.428    &        6.547     \\\\\n",
       "\\textbf{total\\_killed\\_high} &       0.1589  &        0.003     &    52.746  &         0.000        &        0.153    &        0.165     \\\\\n",
       "\\textbf{high\\_ranking}       &      23.5302  &        0.566     &    41.586  &         0.000        &       22.421    &       24.639     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 1371665.459 & \\textbf{  Durbin-Watson:     } &       1.972    \\\\\n",
       "\\textbf{Prob(Omnibus):} &     0.000   & \\textbf{  Jarque-Bera (JB):  } & 645989751.340  \\\\\n",
       "\\textbf{Skew:}          &     5.567   & \\textbf{  Prob(JB):          } &        0.00    \\\\\n",
       "\\textbf{Kurtosis:}      &   117.005   & \\textbf{  Cond. No.          } &    3.43e+15    \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors are robust to cluster correlation (cluster) \\newline\n",
       " [2] The smallest eigenvalue is 1.02e-23. This might indicate that there are \\newline\n",
       " strong multicollinearity problems or that the design matrix is singular."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:               mobility   R-squared:                       0.043\n",
       "Model:                            OLS   Adj. R-squared:                  0.043\n",
       "Method:                 Least Squares   F-statistic:                -8.007e+09\n",
       "Date:                Wed, 21 May 2025   Prob (F-statistic):               1.00\n",
       "Time:                        12:22:26   Log-Likelihood:            -6.3699e+06\n",
       "No. Observations:             1181594   AIC:                         1.274e+07\n",
       "Df Residuals:                 1181549   BIC:                         1.274e+07\n",
       "Df Model:                          44                                         \n",
       "Covariance Type:              cluster                                         \n",
       "=====================================================================================\n",
       "                        coef    std err          z      P>|z|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------------\n",
       "C(strike)[8]          2.8031      0.059     47.693      0.000       2.688       2.918\n",
       "C(strike)[9]         -7.3007      0.024   -307.056      0.000      -7.347      -7.254\n",
       "C(strike)[27]        -6.8232      0.034   -199.831      0.000      -6.890      -6.756\n",
       "C(strike)[32]         5.5182      0.017    315.502      0.000       5.484       5.552\n",
       "C(strike)[36]        -0.6520      0.053    -12.339      0.000      -0.756      -0.548\n",
       "C(strike)[42]        -5.8294      0.061    -95.201      0.000      -5.949      -5.709\n",
       "C(strike)[51]        18.7958      0.050    379.603      0.000      18.699      18.893\n",
       "C(strike)[56]        -1.0318      0.021    -49.073      0.000      -1.073      -0.991\n",
       "C(strike)[59]        -5.2600      0.058    -90.001      0.000      -5.375      -5.145\n",
       "C(strike)[60]         4.1407      0.020    204.181      0.000       4.101       4.180\n",
       "C(strike)[68]        -0.6031      0.059    -10.145      0.000      -0.720      -0.487\n",
       "C(strike)[70]         0.2062      0.059      3.523      0.000       0.091       0.321\n",
       "C(strike)[75]        15.9319      0.054    294.240      0.000      15.826      16.038\n",
       "C(strike)[90]        -8.8401      0.055   -160.488      0.000      -8.948      -8.732\n",
       "C(strike)[92]        -8.3738      0.045   -185.891      0.000      -8.462      -8.286\n",
       "C(strike)[96]        15.9897      0.048    333.037      0.000      15.896      16.084\n",
       "C(strike)[100]        4.8587      0.037    132.407      0.000       4.787       4.931\n",
       "X_1                  -0.8290      0.731     -1.134      0.257      -2.261       0.603\n",
       "X_2                  -1.4974      0.743     -2.016      0.044      -2.953      -0.042\n",
       "X_3                   1.0697      0.596      1.794      0.073      -0.099       2.238\n",
       "X_4                   0.5512      0.491      1.123      0.261      -0.411       1.513\n",
       "X_5                  -0.7341      0.259     -2.830      0.005      -1.242      -0.226\n",
       "X_7                   0.0718      0.308      0.233      0.816      -0.532       0.676\n",
       "X_8                   4.6943      1.192      3.938      0.000       2.358       7.031\n",
       "X_9                   1.7411      0.633      2.752      0.006       0.501       2.981\n",
       "X_10                  1.2915      0.492      2.624      0.009       0.327       2.256\n",
       "X_11                  1.3943      0.480      2.907      0.004       0.454       2.334\n",
       "X_12                 -1.3401      0.472     -2.841      0.004      -2.264      -0.416\n",
       "X_13                  0.9406      0.554      1.698      0.090      -0.145       2.026\n",
       "X_14                  0.6745      0.592      1.139      0.255      -0.486       1.835\n",
       "X_15                  0.9479      0.581      1.630      0.103      -0.192       2.087\n",
       "X_16                  1.7111      0.709      2.412      0.016       0.321       3.101\n",
       "X_17                  1.5232      0.740      2.058      0.040       0.072       2.974\n",
       "X_18                  2.0074      1.362      1.474      0.140      -0.661       4.676\n",
       "X_19                  0.7787      1.542      0.505      0.613      -2.243       3.800\n",
       "X_20                  2.4656      1.570      1.571      0.116      -0.611       5.542\n",
       "X_21                  4.3969      2.489      1.766      0.077      -0.482       9.276\n",
       "X_22                  2.2369      1.032      2.168      0.030       0.214       4.259\n",
       "X_23                  2.2113      1.303      1.697      0.090      -0.343       4.766\n",
       "X_24                  2.9298      0.902      3.249      0.001       1.162       4.697\n",
       "X_25                  2.0018      1.405      1.425      0.154      -0.752       4.755\n",
       "X_26                  0.4972      1.086      0.458      0.647      -1.632       2.626\n",
       "X_27                  2.9825      1.242      2.401      0.016       0.548       5.417\n",
       "X_28                  2.4846      1.100      2.260      0.024       0.329       4.640\n",
       "X_29                  3.4875      1.561      2.234      0.025       0.428       6.547\n",
       "total_killed_high     0.1589      0.003     52.746      0.000       0.153       0.165\n",
       "high_ranking         23.5302      0.566     41.586      0.000      22.421      24.639\n",
       "==============================================================================\n",
       "Omnibus:                  1371665.459   Durbin-Watson:                   1.972\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):        645989751.340\n",
       "Skew:                           5.567   Prob(JB):                         0.00\n",
       "Kurtosis:                     117.005   Cond. No.                     3.43e+15\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors are robust to cluster correlation (cluster)\n",
       "[2] The smallest eigenvalue is 1.02e-23. This might indicate that there are\n",
       "strong multicollinearity problems or that the design matrix is singular.\n",
       "\"\"\""
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# run the event study regression for mobility around strikes\n",
    "# 7 lags and 21 leads\n",
    "# time vars provide the parameters of interest\n",
    "# drop the day = -2 variable for difference in means and to avoid multicollinearity\n",
    "# use strike fixed effects and cluster SE by strike\n",
    "\n",
    "reg = sm.ols('mobility ~ ' + var_form + ' +  C(strike) - 1', \n",
    "             data=df_high_ranking).fit(cov_type='cluster',\n",
    "                          cov_kwds={'groups': df_high_ranking['strike'].values})\n",
    "\n",
    "# save parameters and confidence intervals for plotting\n",
    "conf_int = reg.conf_int()\n",
    "res_highrank = pd.DataFrame({\n",
    "    'params': reg.params,\n",
    "    'ci_l': conf_int[0],  # lower bound\n",
    "    'ci_h': conf_int[1],  # upper bound\n",
    "})\n",
    "\n",
    "# print regression summary\n",
    "reg.summary()\n",
    "# reindex the results to go from day = -7 to day = 21\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>mobility</td>     <th>  R-squared:         </th>  <td>   0.021</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.021</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>     nan</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 21 May 2025</td> <th>  Prob (F-statistic):</th>   <td>   nan</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>12:23:26</td>     <th>  Log-Likelihood:    </th> <td>-1.2989e+07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>2199040</td>     <th>  AIC:               </th>  <td>2.598e+07</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>2198955</td>     <th>  BIC:               </th>  <td>2.598e+07</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    84</td>      <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>       <td>cluster</td>     <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "          <td></td>             <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[1]</th>      <td>   19.9060</td> <td>    0.536</td> <td>   37.109</td> <td> 0.000</td> <td>   18.855</td> <td>   20.957</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[3]</th>      <td>   21.0062</td> <td>    0.551</td> <td>   38.115</td> <td> 0.000</td> <td>   19.926</td> <td>   22.086</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[4]</th>      <td>   28.9779</td> <td>    0.600</td> <td>   48.288</td> <td> 0.000</td> <td>   27.802</td> <td>   30.154</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[5]</th>      <td>   24.2754</td> <td>    0.519</td> <td>   46.739</td> <td> 0.000</td> <td>   23.257</td> <td>   25.293</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[6]</th>      <td>    5.9971</td> <td>    0.019</td> <td>  318.352</td> <td> 0.000</td> <td>    5.960</td> <td>    6.034</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[7]</th>      <td>   16.4096</td> <td>    0.368</td> <td>   44.624</td> <td> 0.000</td> <td>   15.689</td> <td>   17.130</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[10]</th>     <td>   20.3551</td> <td>    0.574</td> <td>   35.439</td> <td> 0.000</td> <td>   19.229</td> <td>   21.481</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[11]</th>     <td>   30.3902</td> <td>    0.603</td> <td>   50.371</td> <td> 0.000</td> <td>   29.208</td> <td>   31.573</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[12]</th>     <td>   13.5389</td> <td>    0.400</td> <td>   33.843</td> <td> 0.000</td> <td>   12.755</td> <td>   14.323</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[14]</th>     <td>   -3.7763</td> <td>    0.176</td> <td>  -21.420</td> <td> 0.000</td> <td>   -4.122</td> <td>   -3.431</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[15]</th>     <td>  -11.1701</td> <td>    0.277</td> <td>  -40.321</td> <td> 0.000</td> <td>  -11.713</td> <td>  -10.627</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[17]</th>     <td>   12.0721</td> <td>    0.371</td> <td>   32.556</td> <td> 0.000</td> <td>   11.345</td> <td>   12.799</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[19]</th>     <td>  -16.8728</td> <td>    0.274</td> <td>  -61.587</td> <td> 0.000</td> <td>  -17.410</td> <td>  -16.336</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[20]</th>     <td>   13.1225</td> <td>    0.394</td> <td>   33.281</td> <td> 0.000</td> <td>   12.350</td> <td>   13.895</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[24]</th>     <td>   26.8806</td> <td>    0.317</td> <td>   84.758</td> <td> 0.000</td> <td>   26.259</td> <td>   27.502</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[26]</th>     <td>   16.7593</td> <td>    0.422</td> <td>   39.674</td> <td> 0.000</td> <td>   15.931</td> <td>   17.587</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[28]</th>     <td>   22.5270</td> <td>    0.610</td> <td>   36.928</td> <td> 0.000</td> <td>   21.331</td> <td>   23.723</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[31]</th>     <td>   15.9417</td> <td>    0.576</td> <td>   27.693</td> <td> 0.000</td> <td>   14.813</td> <td>   17.070</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[33]</th>     <td>  -21.2378</td> <td>    0.422</td> <td>  -50.316</td> <td> 0.000</td> <td>  -22.065</td> <td>  -20.410</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[34]</th>     <td>  -34.6624</td> <td>    0.575</td> <td>  -60.326</td> <td> 0.000</td> <td>  -35.789</td> <td>  -33.536</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[35]</th>     <td>    8.3197</td> <td>    0.429</td> <td>   19.403</td> <td> 0.000</td> <td>    7.479</td> <td>    9.160</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[37]</th>     <td>   -4.7527</td> <td>    0.076</td> <td>  -62.659</td> <td> 0.000</td> <td>   -4.901</td> <td>   -4.604</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[38]</th>     <td>   -5.1890</td> <td>    0.130</td> <td>  -39.894</td> <td> 0.000</td> <td>   -5.444</td> <td>   -4.934</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[39]</th>     <td>  -16.6245</td> <td>    0.275</td> <td>  -60.362</td> <td> 0.000</td> <td>  -17.164</td> <td>  -16.085</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[40]</th>     <td>   11.2084</td> <td>    0.431</td> <td>   26.019</td> <td> 0.000</td> <td>   10.364</td> <td>   12.053</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[41]</th>     <td>  -35.6358</td> <td>    0.656</td> <td>  -54.296</td> <td> 0.000</td> <td>  -36.922</td> <td>  -34.349</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[43]</th>     <td>    4.3490</td> <td>    0.137</td> <td>   31.798</td> <td> 0.000</td> <td>    4.081</td> <td>    4.617</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[45]</th>     <td>   53.4787</td> <td>    0.465</td> <td>  115.112</td> <td> 0.000</td> <td>   52.568</td> <td>   54.389</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[47]</th>     <td>   25.2074</td> <td>    0.393</td> <td>   64.184</td> <td> 0.000</td> <td>   24.438</td> <td>   25.977</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[48]</th>     <td>   11.1270</td> <td>    0.425</td> <td>   26.159</td> <td> 0.000</td> <td>   10.293</td> <td>   11.961</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[49]</th>     <td>   17.7954</td> <td>    0.310</td> <td>   57.490</td> <td> 0.000</td> <td>   17.189</td> <td>   18.402</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[50]</th>     <td>   11.0989</td> <td>    0.106</td> <td>  104.678</td> <td> 0.000</td> <td>   10.891</td> <td>   11.307</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[54]</th>     <td>   23.3336</td> <td>    0.518</td> <td>   45.064</td> <td> 0.000</td> <td>   22.319</td> <td>   24.348</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[57]</th>     <td>   26.0661</td> <td>    0.486</td> <td>   53.632</td> <td> 0.000</td> <td>   25.114</td> <td>   27.019</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[58]</th>     <td>    7.0207</td> <td>    0.161</td> <td>   43.688</td> <td> 0.000</td> <td>    6.706</td> <td>    7.336</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[61]</th>     <td>   17.1124</td> <td>    0.472</td> <td>   36.262</td> <td> 0.000</td> <td>   16.187</td> <td>   18.037</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[62]</th>     <td>   14.3858</td> <td>    0.313</td> <td>   45.976</td> <td> 0.000</td> <td>   13.773</td> <td>   14.999</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[64]</th>     <td>   19.9908</td> <td>    0.311</td> <td>   64.313</td> <td> 0.000</td> <td>   19.382</td> <td>   20.600</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[65]</th>     <td>   28.0739</td> <td>    0.306</td> <td>   91.821</td> <td> 0.000</td> <td>   27.475</td> <td>   28.673</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[67]</th>     <td>   29.4351</td> <td>    0.585</td> <td>   50.292</td> <td> 0.000</td> <td>   28.288</td> <td>   30.582</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[69]</th>     <td>   12.8541</td> <td>    0.131</td> <td>   98.486</td> <td> 0.000</td> <td>   12.598</td> <td>   13.110</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[71]</th>     <td>   24.5113</td> <td>    0.424</td> <td>   57.858</td> <td> 0.000</td> <td>   23.681</td> <td>   25.342</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[72]</th>     <td>   31.6567</td> <td>    0.545</td> <td>   58.067</td> <td> 0.000</td> <td>   30.588</td> <td>   32.725</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[76]</th>     <td>   53.1978</td> <td>    0.401</td> <td>  132.517</td> <td> 0.000</td> <td>   52.411</td> <td>   53.985</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[77]</th>     <td>   15.8031</td> <td>    0.254</td> <td>   62.262</td> <td> 0.000</td> <td>   15.306</td> <td>   16.301</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[78]</th>     <td>   24.5852</td> <td>    0.549</td> <td>   44.768</td> <td> 0.000</td> <td>   23.509</td> <td>   25.662</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[81]</th>     <td>   18.8697</td> <td>    0.128</td> <td>  147.145</td> <td> 0.000</td> <td>   18.618</td> <td>   19.121</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[82]</th>     <td>  -10.9193</td> <td>    0.279</td> <td>  -39.202</td> <td> 0.000</td> <td>  -11.465</td> <td>  -10.373</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[83]</th>     <td>   16.0617</td> <td>    0.336</td> <td>   47.845</td> <td> 0.000</td> <td>   15.404</td> <td>   16.720</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[85]</th>     <td>   19.7771</td> <td>    0.452</td> <td>   43.792</td> <td> 0.000</td> <td>   18.892</td> <td>   20.662</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[87]</th>     <td>   16.0947</td> <td>    0.517</td> <td>   31.157</td> <td> 0.000</td> <td>   15.082</td> <td>   17.107</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[95]</th>     <td>   12.7679</td> <td>    0.468</td> <td>   27.277</td> <td> 0.000</td> <td>   11.851</td> <td>   13.685</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[97]</th>     <td>   10.0791</td> <td>    0.518</td> <td>   19.474</td> <td> 0.000</td> <td>    9.065</td> <td>   11.093</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[101]</th>    <td>   26.2811</td> <td>    0.191</td> <td>  137.616</td> <td> 0.000</td> <td>   25.907</td> <td>   26.655</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[102]</th>    <td>   26.4616</td> <td>    0.428</td> <td>   61.883</td> <td> 0.000</td> <td>   25.623</td> <td>   27.300</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[106]</th>    <td>   11.9966</td> <td>    0.311</td> <td>   38.588</td> <td> 0.000</td> <td>   11.387</td> <td>   12.606</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[107]</th>    <td>   26.4121</td> <td>    0.446</td> <td>   59.210</td> <td> 0.000</td> <td>   25.538</td> <td>   27.286</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_1</th>               <td>   -1.7590</td> <td>    2.638</td> <td>   -0.667</td> <td> 0.505</td> <td>   -6.930</td> <td>    3.412</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_2</th>               <td>   -1.2772</td> <td>    2.853</td> <td>   -0.448</td> <td> 0.654</td> <td>   -6.868</td> <td>    4.314</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_3</th>               <td>   -2.0794</td> <td>    2.676</td> <td>   -0.777</td> <td> 0.437</td> <td>   -7.324</td> <td>    3.165</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_4</th>               <td>    0.0038</td> <td>    1.656</td> <td>    0.002</td> <td> 0.998</td> <td>   -3.242</td> <td>    3.250</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_5</th>               <td>    1.3043</td> <td>    0.630</td> <td>    2.070</td> <td> 0.038</td> <td>    0.069</td> <td>    2.539</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_7</th>               <td>   -1.4508</td> <td>    2.799</td> <td>   -0.518</td> <td> 0.604</td> <td>   -6.937</td> <td>    4.036</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_8</th>               <td>    7.4176</td> <td>    1.676</td> <td>    4.426</td> <td> 0.000</td> <td>    4.133</td> <td>   10.702</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_9</th>               <td>    4.3391</td> <td>    1.375</td> <td>    3.155</td> <td> 0.002</td> <td>    1.644</td> <td>    7.035</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_10</th>              <td>    3.7143</td> <td>    1.384</td> <td>    2.684</td> <td> 0.007</td> <td>    1.002</td> <td>    6.426</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_11</th>              <td>    5.0073</td> <td>    2.387</td> <td>    2.098</td> <td> 0.036</td> <td>    0.329</td> <td>    9.685</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_12</th>              <td>    1.6598</td> <td>    0.770</td> <td>    2.157</td> <td> 0.031</td> <td>    0.151</td> <td>    3.168</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_13</th>              <td>    1.5006</td> <td>    0.792</td> <td>    1.894</td> <td> 0.058</td> <td>   -0.053</td> <td>    3.054</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_14</th>              <td>    2.6330</td> <td>    0.991</td> <td>    2.658</td> <td> 0.008</td> <td>    0.691</td> <td>    4.575</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_15</th>              <td>    2.5265</td> <td>    1.723</td> <td>    1.467</td> <td> 0.142</td> <td>   -0.850</td> <td>    5.903</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_16</th>              <td>    3.3013</td> <td>    1.918</td> <td>    1.722</td> <td> 0.085</td> <td>   -0.457</td> <td>    7.060</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_17</th>              <td>    2.9335</td> <td>    1.672</td> <td>    1.755</td> <td> 0.079</td> <td>   -0.343</td> <td>    6.210</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_18</th>              <td>    2.1434</td> <td>    0.793</td> <td>    2.703</td> <td> 0.007</td> <td>    0.589</td> <td>    3.698</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_19</th>              <td>    1.0484</td> <td>    0.865</td> <td>    1.212</td> <td> 0.225</td> <td>   -0.647</td> <td>    2.743</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_20</th>              <td>    1.8528</td> <td>    1.215</td> <td>    1.525</td> <td> 0.127</td> <td>   -0.529</td> <td>    4.234</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_21</th>              <td>    2.9480</td> <td>    1.102</td> <td>    2.676</td> <td> 0.007</td> <td>    0.789</td> <td>    5.107</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_22</th>              <td>    2.3378</td> <td>    1.251</td> <td>    1.869</td> <td> 0.062</td> <td>   -0.114</td> <td>    4.790</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_23</th>              <td>    2.6970</td> <td>    1.190</td> <td>    2.266</td> <td> 0.023</td> <td>    0.364</td> <td>    5.030</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_24</th>              <td>    2.2185</td> <td>    1.192</td> <td>    1.861</td> <td> 0.063</td> <td>   -0.119</td> <td>    4.555</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_25</th>              <td>    2.6962</td> <td>    0.886</td> <td>    3.044</td> <td> 0.002</td> <td>    0.960</td> <td>    4.432</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_26</th>              <td>    1.5730</td> <td>    0.732</td> <td>    2.149</td> <td> 0.032</td> <td>    0.138</td> <td>    3.008</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_27</th>              <td>    1.0044</td> <td>    1.267</td> <td>    0.793</td> <td> 0.428</td> <td>   -1.478</td> <td>    3.487</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_28</th>              <td>    3.2048</td> <td>    1.369</td> <td>    2.341</td> <td> 0.019</td> <td>    0.522</td> <td>    5.888</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_29</th>              <td>    1.9111</td> <td>    0.947</td> <td>    2.019</td> <td> 0.044</td> <td>    0.055</td> <td>    3.767</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>total_killed_high</th> <td>    1.2644</td> <td>    0.029</td> <td>   43.122</td> <td> 0.000</td> <td>    1.207</td> <td>    1.322</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>high_ranking</th>      <td>         0</td> <td>        0</td> <td>      nan</td> <td>   nan</td> <td>        0</td> <td>        0</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>4310771.972</td> <th>  Durbin-Watson:     </th>    <td>   1.986</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>   <td> 0.000</td>    <th>  Jarque-Bera (JB):  </th> <td>16552615652.378</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>            <td>15.584</td>    <th>  Prob(JB):          </th>    <td>    0.00</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>        <td>426.888</td>   <th>  Cond. No.          </th>    <td>1.18e+16</td>    \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors are robust to cluster correlation (cluster)<br/>[2] The smallest eigenvalue is 6.4e-24. This might indicate that there are<br/>strong multicollinearity problems or that the design matrix is singular."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}      &     mobility     & \\textbf{  R-squared:         } &        0.021     \\\\\n",
       "\\textbf{Model:}              &       OLS        & \\textbf{  Adj. R-squared:    } &        0.021     \\\\\n",
       "\\textbf{Method:}             &  Least Squares   & \\textbf{  F-statistic:       } &          nan     \\\\\n",
       "\\textbf{Date:}               & Wed, 21 May 2025 & \\textbf{  Prob (F-statistic):} &         nan      \\\\\n",
       "\\textbf{Time:}               &     12:23:26     & \\textbf{  Log-Likelihood:    } &   -1.2989e+07    \\\\\n",
       "\\textbf{No. Observations:}   &     2199040      & \\textbf{  AIC:               } &    2.598e+07     \\\\\n",
       "\\textbf{Df Residuals:}       &     2198955      & \\textbf{  BIC:               } &    2.598e+07     \\\\\n",
       "\\textbf{Df Model:}           &          84      & \\textbf{                     } &                  \\\\\n",
       "\\textbf{Covariance Type:}    &     cluster      & \\textbf{                     } &                  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                             & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{C(strike)[1]}        &      19.9060  &        0.536     &    37.109  &         0.000        &       18.855    &       20.957     \\\\\n",
       "\\textbf{C(strike)[3]}        &      21.0062  &        0.551     &    38.115  &         0.000        &       19.926    &       22.086     \\\\\n",
       "\\textbf{C(strike)[4]}        &      28.9779  &        0.600     &    48.288  &         0.000        &       27.802    &       30.154     \\\\\n",
       "\\textbf{C(strike)[5]}        &      24.2754  &        0.519     &    46.739  &         0.000        &       23.257    &       25.293     \\\\\n",
       "\\textbf{C(strike)[6]}        &       5.9971  &        0.019     &   318.352  &         0.000        &        5.960    &        6.034     \\\\\n",
       "\\textbf{C(strike)[7]}        &      16.4096  &        0.368     &    44.624  &         0.000        &       15.689    &       17.130     \\\\\n",
       "\\textbf{C(strike)[10]}       &      20.3551  &        0.574     &    35.439  &         0.000        &       19.229    &       21.481     \\\\\n",
       "\\textbf{C(strike)[11]}       &      30.3902  &        0.603     &    50.371  &         0.000        &       29.208    &       31.573     \\\\\n",
       "\\textbf{C(strike)[12]}       &      13.5389  &        0.400     &    33.843  &         0.000        &       12.755    &       14.323     \\\\\n",
       "\\textbf{C(strike)[14]}       &      -3.7763  &        0.176     &   -21.420  &         0.000        &       -4.122    &       -3.431     \\\\\n",
       "\\textbf{C(strike)[15]}       &     -11.1701  &        0.277     &   -40.321  &         0.000        &      -11.713    &      -10.627     \\\\\n",
       "\\textbf{C(strike)[17]}       &      12.0721  &        0.371     &    32.556  &         0.000        &       11.345    &       12.799     \\\\\n",
       "\\textbf{C(strike)[19]}       &     -16.8728  &        0.274     &   -61.587  &         0.000        &      -17.410    &      -16.336     \\\\\n",
       "\\textbf{C(strike)[20]}       &      13.1225  &        0.394     &    33.281  &         0.000        &       12.350    &       13.895     \\\\\n",
       "\\textbf{C(strike)[24]}       &      26.8806  &        0.317     &    84.758  &         0.000        &       26.259    &       27.502     \\\\\n",
       "\\textbf{C(strike)[26]}       &      16.7593  &        0.422     &    39.674  &         0.000        &       15.931    &       17.587     \\\\\n",
       "\\textbf{C(strike)[28]}       &      22.5270  &        0.610     &    36.928  &         0.000        &       21.331    &       23.723     \\\\\n",
       "\\textbf{C(strike)[31]}       &      15.9417  &        0.576     &    27.693  &         0.000        &       14.813    &       17.070     \\\\\n",
       "\\textbf{C(strike)[33]}       &     -21.2378  &        0.422     &   -50.316  &         0.000        &      -22.065    &      -20.410     \\\\\n",
       "\\textbf{C(strike)[34]}       &     -34.6624  &        0.575     &   -60.326  &         0.000        &      -35.789    &      -33.536     \\\\\n",
       "\\textbf{C(strike)[35]}       &       8.3197  &        0.429     &    19.403  &         0.000        &        7.479    &        9.160     \\\\\n",
       "\\textbf{C(strike)[37]}       &      -4.7527  &        0.076     &   -62.659  &         0.000        &       -4.901    &       -4.604     \\\\\n",
       "\\textbf{C(strike)[38]}       &      -5.1890  &        0.130     &   -39.894  &         0.000        &       -5.444    &       -4.934     \\\\\n",
       "\\textbf{C(strike)[39]}       &     -16.6245  &        0.275     &   -60.362  &         0.000        &      -17.164    &      -16.085     \\\\\n",
       "\\textbf{C(strike)[40]}       &      11.2084  &        0.431     &    26.019  &         0.000        &       10.364    &       12.053     \\\\\n",
       "\\textbf{C(strike)[41]}       &     -35.6358  &        0.656     &   -54.296  &         0.000        &      -36.922    &      -34.349     \\\\\n",
       "\\textbf{C(strike)[43]}       &       4.3490  &        0.137     &    31.798  &         0.000        &        4.081    &        4.617     \\\\\n",
       "\\textbf{C(strike)[45]}       &      53.4787  &        0.465     &   115.112  &         0.000        &       52.568    &       54.389     \\\\\n",
       "\\textbf{C(strike)[47]}       &      25.2074  &        0.393     &    64.184  &         0.000        &       24.438    &       25.977     \\\\\n",
       "\\textbf{C(strike)[48]}       &      11.1270  &        0.425     &    26.159  &         0.000        &       10.293    &       11.961     \\\\\n",
       "\\textbf{C(strike)[49]}       &      17.7954  &        0.310     &    57.490  &         0.000        &       17.189    &       18.402     \\\\\n",
       "\\textbf{C(strike)[50]}       &      11.0989  &        0.106     &   104.678  &         0.000        &       10.891    &       11.307     \\\\\n",
       "\\textbf{C(strike)[54]}       &      23.3336  &        0.518     &    45.064  &         0.000        &       22.319    &       24.348     \\\\\n",
       "\\textbf{C(strike)[57]}       &      26.0661  &        0.486     &    53.632  &         0.000        &       25.114    &       27.019     \\\\\n",
       "\\textbf{C(strike)[58]}       &       7.0207  &        0.161     &    43.688  &         0.000        &        6.706    &        7.336     \\\\\n",
       "\\textbf{C(strike)[61]}       &      17.1124  &        0.472     &    36.262  &         0.000        &       16.187    &       18.037     \\\\\n",
       "\\textbf{C(strike)[62]}       &      14.3858  &        0.313     &    45.976  &         0.000        &       13.773    &       14.999     \\\\\n",
       "\\textbf{C(strike)[64]}       &      19.9908  &        0.311     &    64.313  &         0.000        &       19.382    &       20.600     \\\\\n",
       "\\textbf{C(strike)[65]}       &      28.0739  &        0.306     &    91.821  &         0.000        &       27.475    &       28.673     \\\\\n",
       "\\textbf{C(strike)[67]}       &      29.4351  &        0.585     &    50.292  &         0.000        &       28.288    &       30.582     \\\\\n",
       "\\textbf{C(strike)[69]}       &      12.8541  &        0.131     &    98.486  &         0.000        &       12.598    &       13.110     \\\\\n",
       "\\textbf{C(strike)[71]}       &      24.5113  &        0.424     &    57.858  &         0.000        &       23.681    &       25.342     \\\\\n",
       "\\textbf{C(strike)[72]}       &      31.6567  &        0.545     &    58.067  &         0.000        &       30.588    &       32.725     \\\\\n",
       "\\textbf{C(strike)[76]}       &      53.1978  &        0.401     &   132.517  &         0.000        &       52.411    &       53.985     \\\\\n",
       "\\textbf{C(strike)[77]}       &      15.8031  &        0.254     &    62.262  &         0.000        &       15.306    &       16.301     \\\\\n",
       "\\textbf{C(strike)[78]}       &      24.5852  &        0.549     &    44.768  &         0.000        &       23.509    &       25.662     \\\\\n",
       "\\textbf{C(strike)[81]}       &      18.8697  &        0.128     &   147.145  &         0.000        &       18.618    &       19.121     \\\\\n",
       "\\textbf{C(strike)[82]}       &     -10.9193  &        0.279     &   -39.202  &         0.000        &      -11.465    &      -10.373     \\\\\n",
       "\\textbf{C(strike)[83]}       &      16.0617  &        0.336     &    47.845  &         0.000        &       15.404    &       16.720     \\\\\n",
       "\\textbf{C(strike)[85]}       &      19.7771  &        0.452     &    43.792  &         0.000        &       18.892    &       20.662     \\\\\n",
       "\\textbf{C(strike)[87]}       &      16.0947  &        0.517     &    31.157  &         0.000        &       15.082    &       17.107     \\\\\n",
       "\\textbf{C(strike)[95]}       &      12.7679  &        0.468     &    27.277  &         0.000        &       11.851    &       13.685     \\\\\n",
       "\\textbf{C(strike)[97]}       &      10.0791  &        0.518     &    19.474  &         0.000        &        9.065    &       11.093     \\\\\n",
       "\\textbf{C(strike)[101]}      &      26.2811  &        0.191     &   137.616  &         0.000        &       25.907    &       26.655     \\\\\n",
       "\\textbf{C(strike)[102]}      &      26.4616  &        0.428     &    61.883  &         0.000        &       25.623    &       27.300     \\\\\n",
       "\\textbf{C(strike)[106]}      &      11.9966  &        0.311     &    38.588  &         0.000        &       11.387    &       12.606     \\\\\n",
       "\\textbf{C(strike)[107]}      &      26.4121  &        0.446     &    59.210  &         0.000        &       25.538    &       27.286     \\\\\n",
       "\\textbf{X\\_1}                &      -1.7590  &        2.638     &    -0.667  &         0.505        &       -6.930    &        3.412     \\\\\n",
       "\\textbf{X\\_2}                &      -1.2772  &        2.853     &    -0.448  &         0.654        &       -6.868    &        4.314     \\\\\n",
       "\\textbf{X\\_3}                &      -2.0794  &        2.676     &    -0.777  &         0.437        &       -7.324    &        3.165     \\\\\n",
       "\\textbf{X\\_4}                &       0.0038  &        1.656     &     0.002  &         0.998        &       -3.242    &        3.250     \\\\\n",
       "\\textbf{X\\_5}                &       1.3043  &        0.630     &     2.070  &         0.038        &        0.069    &        2.539     \\\\\n",
       "\\textbf{X\\_7}                &      -1.4508  &        2.799     &    -0.518  &         0.604        &       -6.937    &        4.036     \\\\\n",
       "\\textbf{X\\_8}                &       7.4176  &        1.676     &     4.426  &         0.000        &        4.133    &       10.702     \\\\\n",
       "\\textbf{X\\_9}                &       4.3391  &        1.375     &     3.155  &         0.002        &        1.644    &        7.035     \\\\\n",
       "\\textbf{X\\_10}               &       3.7143  &        1.384     &     2.684  &         0.007        &        1.002    &        6.426     \\\\\n",
       "\\textbf{X\\_11}               &       5.0073  &        2.387     &     2.098  &         0.036        &        0.329    &        9.685     \\\\\n",
       "\\textbf{X\\_12}               &       1.6598  &        0.770     &     2.157  &         0.031        &        0.151    &        3.168     \\\\\n",
       "\\textbf{X\\_13}               &       1.5006  &        0.792     &     1.894  &         0.058        &       -0.053    &        3.054     \\\\\n",
       "\\textbf{X\\_14}               &       2.6330  &        0.991     &     2.658  &         0.008        &        0.691    &        4.575     \\\\\n",
       "\\textbf{X\\_15}               &       2.5265  &        1.723     &     1.467  &         0.142        &       -0.850    &        5.903     \\\\\n",
       "\\textbf{X\\_16}               &       3.3013  &        1.918     &     1.722  &         0.085        &       -0.457    &        7.060     \\\\\n",
       "\\textbf{X\\_17}               &       2.9335  &        1.672     &     1.755  &         0.079        &       -0.343    &        6.210     \\\\\n",
       "\\textbf{X\\_18}               &       2.1434  &        0.793     &     2.703  &         0.007        &        0.589    &        3.698     \\\\\n",
       "\\textbf{X\\_19}               &       1.0484  &        0.865     &     1.212  &         0.225        &       -0.647    &        2.743     \\\\\n",
       "\\textbf{X\\_20}               &       1.8528  &        1.215     &     1.525  &         0.127        &       -0.529    &        4.234     \\\\\n",
       "\\textbf{X\\_21}               &       2.9480  &        1.102     &     2.676  &         0.007        &        0.789    &        5.107     \\\\\n",
       "\\textbf{X\\_22}               &       2.3378  &        1.251     &     1.869  &         0.062        &       -0.114    &        4.790     \\\\\n",
       "\\textbf{X\\_23}               &       2.6970  &        1.190     &     2.266  &         0.023        &        0.364    &        5.030     \\\\\n",
       "\\textbf{X\\_24}               &       2.2185  &        1.192     &     1.861  &         0.063        &       -0.119    &        4.555     \\\\\n",
       "\\textbf{X\\_25}               &       2.6962  &        0.886     &     3.044  &         0.002        &        0.960    &        4.432     \\\\\n",
       "\\textbf{X\\_26}               &       1.5730  &        0.732     &     2.149  &         0.032        &        0.138    &        3.008     \\\\\n",
       "\\textbf{X\\_27}               &       1.0044  &        1.267     &     0.793  &         0.428        &       -1.478    &        3.487     \\\\\n",
       "\\textbf{X\\_28}               &       3.2048  &        1.369     &     2.341  &         0.019        &        0.522    &        5.888     \\\\\n",
       "\\textbf{X\\_29}               &       1.9111  &        0.947     &     2.019  &         0.044        &        0.055    &        3.767     \\\\\n",
       "\\textbf{total\\_killed\\_high} &       1.2644  &        0.029     &    43.122  &         0.000        &        1.207    &        1.322     \\\\\n",
       "\\textbf{high\\_ranking}       &            0  &            0     &       nan  &           nan        &            0    &            0     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 4310771.972 & \\textbf{  Durbin-Watson:     } &        1.986     \\\\\n",
       "\\textbf{Prob(Omnibus):} &     0.000   & \\textbf{  Jarque-Bera (JB):  } & 16552615652.378  \\\\\n",
       "\\textbf{Skew:}          &    15.584   & \\textbf{  Prob(JB):          } &         0.00     \\\\\n",
       "\\textbf{Kurtosis:}      &   426.888   & \\textbf{  Cond. No.          } &     1.18e+16     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors are robust to cluster correlation (cluster) \\newline\n",
       " [2] The smallest eigenvalue is 6.4e-24. This might indicate that there are \\newline\n",
       " strong multicollinearity problems or that the design matrix is singular."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:               mobility   R-squared:                       0.021\n",
       "Model:                            OLS   Adj. R-squared:                  0.021\n",
       "Method:                 Least Squares   F-statistic:                       nan\n",
       "Date:                Wed, 21 May 2025   Prob (F-statistic):                nan\n",
       "Time:                        12:23:26   Log-Likelihood:            -1.2989e+07\n",
       "No. Observations:             2199040   AIC:                         2.598e+07\n",
       "Df Residuals:                 2198955   BIC:                         2.598e+07\n",
       "Df Model:                          84                                         \n",
       "Covariance Type:              cluster                                         \n",
       "=====================================================================================\n",
       "                        coef    std err          z      P>|z|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------------\n",
       "C(strike)[1]         19.9060      0.536     37.109      0.000      18.855      20.957\n",
       "C(strike)[3]         21.0062      0.551     38.115      0.000      19.926      22.086\n",
       "C(strike)[4]         28.9779      0.600     48.288      0.000      27.802      30.154\n",
       "C(strike)[5]         24.2754      0.519     46.739      0.000      23.257      25.293\n",
       "C(strike)[6]          5.9971      0.019    318.352      0.000       5.960       6.034\n",
       "C(strike)[7]         16.4096      0.368     44.624      0.000      15.689      17.130\n",
       "C(strike)[10]        20.3551      0.574     35.439      0.000      19.229      21.481\n",
       "C(strike)[11]        30.3902      0.603     50.371      0.000      29.208      31.573\n",
       "C(strike)[12]        13.5389      0.400     33.843      0.000      12.755      14.323\n",
       "C(strike)[14]        -3.7763      0.176    -21.420      0.000      -4.122      -3.431\n",
       "C(strike)[15]       -11.1701      0.277    -40.321      0.000     -11.713     -10.627\n",
       "C(strike)[17]        12.0721      0.371     32.556      0.000      11.345      12.799\n",
       "C(strike)[19]       -16.8728      0.274    -61.587      0.000     -17.410     -16.336\n",
       "C(strike)[20]        13.1225      0.394     33.281      0.000      12.350      13.895\n",
       "C(strike)[24]        26.8806      0.317     84.758      0.000      26.259      27.502\n",
       "C(strike)[26]        16.7593      0.422     39.674      0.000      15.931      17.587\n",
       "C(strike)[28]        22.5270      0.610     36.928      0.000      21.331      23.723\n",
       "C(strike)[31]        15.9417      0.576     27.693      0.000      14.813      17.070\n",
       "C(strike)[33]       -21.2378      0.422    -50.316      0.000     -22.065     -20.410\n",
       "C(strike)[34]       -34.6624      0.575    -60.326      0.000     -35.789     -33.536\n",
       "C(strike)[35]         8.3197      0.429     19.403      0.000       7.479       9.160\n",
       "C(strike)[37]        -4.7527      0.076    -62.659      0.000      -4.901      -4.604\n",
       "C(strike)[38]        -5.1890      0.130    -39.894      0.000      -5.444      -4.934\n",
       "C(strike)[39]       -16.6245      0.275    -60.362      0.000     -17.164     -16.085\n",
       "C(strike)[40]        11.2084      0.431     26.019      0.000      10.364      12.053\n",
       "C(strike)[41]       -35.6358      0.656    -54.296      0.000     -36.922     -34.349\n",
       "C(strike)[43]         4.3490      0.137     31.798      0.000       4.081       4.617\n",
       "C(strike)[45]        53.4787      0.465    115.112      0.000      52.568      54.389\n",
       "C(strike)[47]        25.2074      0.393     64.184      0.000      24.438      25.977\n",
       "C(strike)[48]        11.1270      0.425     26.159      0.000      10.293      11.961\n",
       "C(strike)[49]        17.7954      0.310     57.490      0.000      17.189      18.402\n",
       "C(strike)[50]        11.0989      0.106    104.678      0.000      10.891      11.307\n",
       "C(strike)[54]        23.3336      0.518     45.064      0.000      22.319      24.348\n",
       "C(strike)[57]        26.0661      0.486     53.632      0.000      25.114      27.019\n",
       "C(strike)[58]         7.0207      0.161     43.688      0.000       6.706       7.336\n",
       "C(strike)[61]        17.1124      0.472     36.262      0.000      16.187      18.037\n",
       "C(strike)[62]        14.3858      0.313     45.976      0.000      13.773      14.999\n",
       "C(strike)[64]        19.9908      0.311     64.313      0.000      19.382      20.600\n",
       "C(strike)[65]        28.0739      0.306     91.821      0.000      27.475      28.673\n",
       "C(strike)[67]        29.4351      0.585     50.292      0.000      28.288      30.582\n",
       "C(strike)[69]        12.8541      0.131     98.486      0.000      12.598      13.110\n",
       "C(strike)[71]        24.5113      0.424     57.858      0.000      23.681      25.342\n",
       "C(strike)[72]        31.6567      0.545     58.067      0.000      30.588      32.725\n",
       "C(strike)[76]        53.1978      0.401    132.517      0.000      52.411      53.985\n",
       "C(strike)[77]        15.8031      0.254     62.262      0.000      15.306      16.301\n",
       "C(strike)[78]        24.5852      0.549     44.768      0.000      23.509      25.662\n",
       "C(strike)[81]        18.8697      0.128    147.145      0.000      18.618      19.121\n",
       "C(strike)[82]       -10.9193      0.279    -39.202      0.000     -11.465     -10.373\n",
       "C(strike)[83]        16.0617      0.336     47.845      0.000      15.404      16.720\n",
       "C(strike)[85]        19.7771      0.452     43.792      0.000      18.892      20.662\n",
       "C(strike)[87]        16.0947      0.517     31.157      0.000      15.082      17.107\n",
       "C(strike)[95]        12.7679      0.468     27.277      0.000      11.851      13.685\n",
       "C(strike)[97]        10.0791      0.518     19.474      0.000       9.065      11.093\n",
       "C(strike)[101]       26.2811      0.191    137.616      0.000      25.907      26.655\n",
       "C(strike)[102]       26.4616      0.428     61.883      0.000      25.623      27.300\n",
       "C(strike)[106]       11.9966      0.311     38.588      0.000      11.387      12.606\n",
       "C(strike)[107]       26.4121      0.446     59.210      0.000      25.538      27.286\n",
       "X_1                  -1.7590      2.638     -0.667      0.505      -6.930       3.412\n",
       "X_2                  -1.2772      2.853     -0.448      0.654      -6.868       4.314\n",
       "X_3                  -2.0794      2.676     -0.777      0.437      -7.324       3.165\n",
       "X_4                   0.0038      1.656      0.002      0.998      -3.242       3.250\n",
       "X_5                   1.3043      0.630      2.070      0.038       0.069       2.539\n",
       "X_7                  -1.4508      2.799     -0.518      0.604      -6.937       4.036\n",
       "X_8                   7.4176      1.676      4.426      0.000       4.133      10.702\n",
       "X_9                   4.3391      1.375      3.155      0.002       1.644       7.035\n",
       "X_10                  3.7143      1.384      2.684      0.007       1.002       6.426\n",
       "X_11                  5.0073      2.387      2.098      0.036       0.329       9.685\n",
       "X_12                  1.6598      0.770      2.157      0.031       0.151       3.168\n",
       "X_13                  1.5006      0.792      1.894      0.058      -0.053       3.054\n",
       "X_14                  2.6330      0.991      2.658      0.008       0.691       4.575\n",
       "X_15                  2.5265      1.723      1.467      0.142      -0.850       5.903\n",
       "X_16                  3.3013      1.918      1.722      0.085      -0.457       7.060\n",
       "X_17                  2.9335      1.672      1.755      0.079      -0.343       6.210\n",
       "X_18                  2.1434      0.793      2.703      0.007       0.589       3.698\n",
       "X_19                  1.0484      0.865      1.212      0.225      -0.647       2.743\n",
       "X_20                  1.8528      1.215      1.525      0.127      -0.529       4.234\n",
       "X_21                  2.9480      1.102      2.676      0.007       0.789       5.107\n",
       "X_22                  2.3378      1.251      1.869      0.062      -0.114       4.790\n",
       "X_23                  2.6970      1.190      2.266      0.023       0.364       5.030\n",
       "X_24                  2.2185      1.192      1.861      0.063      -0.119       4.555\n",
       "X_25                  2.6962      0.886      3.044      0.002       0.960       4.432\n",
       "X_26                  1.5730      0.732      2.149      0.032       0.138       3.008\n",
       "X_27                  1.0044      1.267      0.793      0.428      -1.478       3.487\n",
       "X_28                  3.2048      1.369      2.341      0.019       0.522       5.888\n",
       "X_29                  1.9111      0.947      2.019      0.044       0.055       3.767\n",
       "total_killed_high     1.2644      0.029     43.122      0.000       1.207       1.322\n",
       "high_ranking               0          0        nan        nan           0           0\n",
       "==============================================================================\n",
       "Omnibus:                  4310771.972   Durbin-Watson:                   1.986\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):      16552615652.378\n",
       "Skew:                          15.584   Prob(JB):                         0.00\n",
       "Kurtosis:                     426.888   Cond. No.                     1.18e+16\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors are robust to cluster correlation (cluster)\n",
       "[2] The smallest eigenvalue is 6.4e-24. This might indicate that there are\n",
       "strong multicollinearity problems or that the design matrix is singular.\n",
       "\"\"\""
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# run the event study regression for mobility around strikes\n",
    "# 7 lags and 21 leads\n",
    "# time vars provide the parameters of interest\n",
    "# drop the day = -2 variable for difference in means and to avoid multicollinearity\n",
    "# use strike fixed effects and cluster SE by strike\n",
    "\n",
    "reg = sm.ols('mobility ~ ' + var_form + ' +  C(strike) - 1', \n",
    "             data=df_low_ranking).fit(cov_type='cluster',\n",
    "                          cov_kwds={'groups': df_low_ranking['strike'].values})\n",
    "\n",
    "# save parameters and confidence intervals for plotting\n",
    "conf_int = reg.conf_int()\n",
    "res_lowrank = pd.DataFrame({\n",
    "    'params': reg.params,\n",
    "    'ci_l': conf_int[0],  # lower bound\n",
    "    'ci_h': conf_int[1],  # upper bound\n",
    "})\n",
    "\n",
    "# print regression summary\n",
    "reg.summary()\n",
    "# reindex the results to go from day = -7 to day = 21\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "event_res_lowrank = res_lowrank[res_lowrank.index.to_series().str.startswith(\"X_\")].copy()\n",
    "event_res_lowrank = event_res_lowrank.reset_index().rename(columns={'index': 'varname'})\n",
    "event_res_lowrank['day'] = event_res_lowrank['varname'].str.extract(r'X_(\\d+)').astype(float).astype('Int64') - 8\n",
    "\n",
    "event_res_highrank = res_highrank[res_highrank.index.to_series().str.startswith(\"X_\")].copy()\n",
    "event_res_highrank = event_res_highrank.reset_index().rename(columns={'index': 'varname'})\n",
    "event_res_highrank['day'] = event_res_highrank['varname'].str.extract(r'X_(\\d+)').astype(float).astype('Int64') - 8\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_two_group(event_res_highrank, event_res_lowrank,\n",
    "                       label1='High Ranking Militants Killed',\n",
    "                       label2='No High Ranking Militants Killed',\n",
    "                       color1='C0', color2='C1',\n",
    "                       ylabel='Distance (km)',\n",
    "                       outfile='figures/figureX_militantrank.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    74.00000\n",
       "mean      0.22973\n",
       "std       0.42353\n",
       "min       0.00000\n",
       "25%       0.00000\n",
       "50%       0.00000\n",
       "75%       0.00000\n",
       "max       1.00000\n",
       "Name: high_ranking, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strikes['high_ranking'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure S14A: Mobility Results with Strike Quantile Adjustment (timing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create separate dataframes for strikes in different time periods\n",
    "df_period1 = df[df['strike_quantile'] == 0]\n",
    "df_period2 = df[df['strike_quantile'] == 1]\n",
    "df_period3 = df[df['strike_quantile'] == 2]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>mobility</td>     <th>  R-squared:         </th>  <td>   0.010</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.009</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>     nan</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 11 Jun 2025</td> <th>  Prob (F-statistic):</th>   <td>   nan</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>14:33:30</td>     <th>  Log-Likelihood:    </th> <td>-3.8240e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>715218</td>      <th>  AIC:               </th>  <td>7.648e+06</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>715165</td>      <th>  BIC:               </th>  <td>7.649e+06</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    52</td>      <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>       <td>cluster</td>     <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td></td>           <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[1]</th>  <td>   23.4435</td> <td>    0.606</td> <td>   38.717</td> <td> 0.000</td> <td>   22.257</td> <td>   24.630</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[3]</th>  <td>   24.5668</td> <td>    0.605</td> <td>   40.581</td> <td> 0.000</td> <td>   23.380</td> <td>   25.753</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[4]</th>  <td>   29.9910</td> <td>    0.604</td> <td>   49.617</td> <td> 0.000</td> <td>   28.806</td> <td>   31.176</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[5]</th>  <td>   29.0898</td> <td>    0.607</td> <td>   47.949</td> <td> 0.000</td> <td>   27.901</td> <td>   30.279</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[6]</th>  <td>   32.2995</td> <td>    0.606</td> <td>   53.277</td> <td> 0.000</td> <td>   31.111</td> <td>   33.488</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[7]</th>  <td>   27.5372</td> <td>    0.606</td> <td>   45.455</td> <td> 0.000</td> <td>   26.350</td> <td>   28.725</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[8]</th>  <td>   27.1364</td> <td>    0.605</td> <td>   44.837</td> <td> 0.000</td> <td>   25.950</td> <td>   28.323</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[9]</th>  <td>   18.6165</td> <td>    0.605</td> <td>   30.757</td> <td> 0.000</td> <td>   17.430</td> <td>   19.803</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[10]</th> <td>   22.6344</td> <td>    0.606</td> <td>   37.364</td> <td> 0.000</td> <td>   21.447</td> <td>   23.822</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[11]</th> <td>   31.4049</td> <td>    0.606</td> <td>   51.841</td> <td> 0.000</td> <td>   30.218</td> <td>   32.592</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[12]</th> <td>   22.0904</td> <td>    0.604</td> <td>   36.551</td> <td> 0.000</td> <td>   20.906</td> <td>   23.275</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[14]</th> <td>   16.2298</td> <td>    0.605</td> <td>   26.810</td> <td> 0.000</td> <td>   15.043</td> <td>   17.416</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[15]</th> <td>   27.7744</td> <td>    0.606</td> <td>   45.811</td> <td> 0.000</td> <td>   26.586</td> <td>   28.963</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[17]</th> <td>   23.2047</td> <td>    0.606</td> <td>   38.319</td> <td> 0.000</td> <td>   22.018</td> <td>   24.392</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[19]</th> <td>   22.0799</td> <td>    0.605</td> <td>   36.510</td> <td> 0.000</td> <td>   20.895</td> <td>   23.265</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[20]</th> <td>   22.9821</td> <td>    0.606</td> <td>   37.950</td> <td> 0.000</td> <td>   21.795</td> <td>   24.169</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[24]</th> <td>   40.5481</td> <td>    0.608</td> <td>   66.689</td> <td> 0.000</td> <td>   39.356</td> <td>   41.740</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[26]</th> <td>   24.0630</td> <td>    0.604</td> <td>   39.861</td> <td> 0.000</td> <td>   22.880</td> <td>   25.246</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[27]</th> <td>   22.4392</td> <td>    0.606</td> <td>   37.017</td> <td> 0.000</td> <td>   21.251</td> <td>   23.627</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[28]</th> <td>   23.5532</td> <td>    0.606</td> <td>   38.856</td> <td> 0.000</td> <td>   22.365</td> <td>   24.741</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[31]</th> <td>   18.2241</td> <td>    0.605</td> <td>   30.104</td> <td> 0.000</td> <td>   17.038</td> <td>   19.411</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[32]</th> <td>   31.9170</td> <td>    0.606</td> <td>   52.697</td> <td> 0.000</td> <td>   30.730</td> <td>   33.104</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[33]</th> <td>   24.0314</td> <td>    0.606</td> <td>   39.665</td> <td> 0.000</td> <td>   22.844</td> <td>   25.219</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[34]</th> <td>   16.9191</td> <td>    0.606</td> <td>   27.933</td> <td> 0.000</td> <td>   15.732</td> <td>   18.106</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[35]</th> <td>   16.9226</td> <td>    0.606</td> <td>   27.929</td> <td> 0.000</td> <td>   15.735</td> <td>   18.110</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_1</th>           <td>   -0.5667</td> <td>    1.763</td> <td>   -0.321</td> <td> 0.748</td> <td>   -4.022</td> <td>    2.889</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_2</th>           <td>    1.2592</td> <td>    1.270</td> <td>    0.992</td> <td> 0.321</td> <td>   -1.229</td> <td>    3.748</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_3</th>           <td>    0.3396</td> <td>    0.977</td> <td>    0.348</td> <td> 0.728</td> <td>   -1.575</td> <td>    2.254</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_4</th>           <td>   -0.0628</td> <td>    0.757</td> <td>   -0.083</td> <td> 0.934</td> <td>   -1.547</td> <td>    1.421</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_5</th>           <td>    0.4180</td> <td>    0.813</td> <td>    0.514</td> <td> 0.607</td> <td>   -1.176</td> <td>    2.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_7</th>           <td>   -0.1092</td> <td>    1.038</td> <td>   -0.105</td> <td> 0.916</td> <td>   -2.144</td> <td>    1.926</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_8</th>           <td>    2.6705</td> <td>    1.188</td> <td>    2.247</td> <td> 0.025</td> <td>    0.342</td> <td>    4.999</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_9</th>           <td>    2.6617</td> <td>    1.261</td> <td>    2.110</td> <td> 0.035</td> <td>    0.189</td> <td>    5.134</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_10</th>          <td>    2.5699</td> <td>    1.383</td> <td>    1.858</td> <td> 0.063</td> <td>   -0.141</td> <td>    5.281</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_11</th>          <td>    1.5476</td> <td>    0.731</td> <td>    2.117</td> <td> 0.034</td> <td>    0.115</td> <td>    2.980</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_12</th>          <td>    0.0056</td> <td>    0.704</td> <td>    0.008</td> <td> 0.994</td> <td>   -1.374</td> <td>    1.385</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_13</th>          <td>    0.8569</td> <td>    1.048</td> <td>    0.818</td> <td> 0.413</td> <td>   -1.197</td> <td>    2.911</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_14</th>          <td>    1.8323</td> <td>    1.232</td> <td>    1.487</td> <td> 0.137</td> <td>   -0.582</td> <td>    4.247</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_15</th>          <td>   -0.7909</td> <td>    1.112</td> <td>   -0.711</td> <td> 0.477</td> <td>   -2.971</td> <td>    1.389</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_16</th>          <td>    0.2300</td> <td>    0.688</td> <td>    0.334</td> <td> 0.738</td> <td>   -1.119</td> <td>    1.579</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_17</th>          <td>    0.8072</td> <td>    0.908</td> <td>    0.889</td> <td> 0.374</td> <td>   -0.973</td> <td>    2.587</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_18</th>          <td>    1.1577</td> <td>    0.892</td> <td>    1.297</td> <td> 0.194</td> <td>   -0.591</td> <td>    2.906</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_19</th>          <td>    0.2258</td> <td>    0.873</td> <td>    0.259</td> <td> 0.796</td> <td>   -1.486</td> <td>    1.937</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_20</th>          <td>    1.8895</td> <td>    0.991</td> <td>    1.906</td> <td> 0.057</td> <td>   -0.053</td> <td>    3.832</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_21</th>          <td>    2.1473</td> <td>    1.121</td> <td>    1.915</td> <td> 0.055</td> <td>   -0.050</td> <td>    4.345</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_22</th>          <td>    0.6174</td> <td>    1.237</td> <td>    0.499</td> <td> 0.618</td> <td>   -1.808</td> <td>    3.042</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_23</th>          <td>    1.5753</td> <td>    0.629</td> <td>    2.505</td> <td> 0.012</td> <td>    0.343</td> <td>    2.808</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_24</th>          <td>    1.5368</td> <td>    0.995</td> <td>    1.544</td> <td> 0.123</td> <td>   -0.414</td> <td>    3.487</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_25</th>          <td>    1.5337</td> <td>    1.016</td> <td>    1.510</td> <td> 0.131</td> <td>   -0.457</td> <td>    3.524</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_26</th>          <td>    0.1641</td> <td>    0.937</td> <td>    0.175</td> <td> 0.861</td> <td>   -1.672</td> <td>    2.000</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_27</th>          <td>   -0.1996</td> <td>    0.494</td> <td>   -0.404</td> <td> 0.686</td> <td>   -1.169</td> <td>    0.769</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_28</th>          <td>    2.3399</td> <td>    1.447</td> <td>    1.617</td> <td> 0.106</td> <td>   -0.497</td> <td>    5.177</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_29</th>          <td>   -0.4404</td> <td>    0.818</td> <td>   -0.539</td> <td> 0.590</td> <td>   -2.043</td> <td>    1.162</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>674661.180</td> <th>  Durbin-Watson:     </th>   <td>   1.994</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>   <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>39069914.316</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>            <td> 4.513</td>   <th>  Prob(JB):          </th>   <td>    0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>        <td>38.065</td>   <th>  Cond. No.          </th>   <td>    16.1</td>  \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors are robust to cluster correlation (cluster)"
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &     mobility     & \\textbf{  R-squared:         } &      0.010    \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &      0.009    \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &        nan    \\\\\n",
       "\\textbf{Date:}             & Wed, 11 Jun 2025 & \\textbf{  Prob (F-statistic):} &       nan     \\\\\n",
       "\\textbf{Time:}             &     14:33:30     & \\textbf{  Log-Likelihood:    } & -3.8240e+06   \\\\\n",
       "\\textbf{No. Observations:} &      715218      & \\textbf{  AIC:               } &  7.648e+06    \\\\\n",
       "\\textbf{Df Residuals:}     &      715165      & \\textbf{  BIC:               } &  7.649e+06    \\\\\n",
       "\\textbf{Df Model:}         &          52      & \\textbf{                     } &               \\\\\n",
       "\\textbf{Covariance Type:}  &     cluster      & \\textbf{                     } &               \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                       & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{C(strike)[1]}  &      23.4435  &        0.606     &    38.717  &         0.000        &       22.257    &       24.630     \\\\\n",
       "\\textbf{C(strike)[3]}  &      24.5668  &        0.605     &    40.581  &         0.000        &       23.380    &       25.753     \\\\\n",
       "\\textbf{C(strike)[4]}  &      29.9910  &        0.604     &    49.617  &         0.000        &       28.806    &       31.176     \\\\\n",
       "\\textbf{C(strike)[5]}  &      29.0898  &        0.607     &    47.949  &         0.000        &       27.901    &       30.279     \\\\\n",
       "\\textbf{C(strike)[6]}  &      32.2995  &        0.606     &    53.277  &         0.000        &       31.111    &       33.488     \\\\\n",
       "\\textbf{C(strike)[7]}  &      27.5372  &        0.606     &    45.455  &         0.000        &       26.350    &       28.725     \\\\\n",
       "\\textbf{C(strike)[8]}  &      27.1364  &        0.605     &    44.837  &         0.000        &       25.950    &       28.323     \\\\\n",
       "\\textbf{C(strike)[9]}  &      18.6165  &        0.605     &    30.757  &         0.000        &       17.430    &       19.803     \\\\\n",
       "\\textbf{C(strike)[10]} &      22.6344  &        0.606     &    37.364  &         0.000        &       21.447    &       23.822     \\\\\n",
       "\\textbf{C(strike)[11]} &      31.4049  &        0.606     &    51.841  &         0.000        &       30.218    &       32.592     \\\\\n",
       "\\textbf{C(strike)[12]} &      22.0904  &        0.604     &    36.551  &         0.000        &       20.906    &       23.275     \\\\\n",
       "\\textbf{C(strike)[14]} &      16.2298  &        0.605     &    26.810  &         0.000        &       15.043    &       17.416     \\\\\n",
       "\\textbf{C(strike)[15]} &      27.7744  &        0.606     &    45.811  &         0.000        &       26.586    &       28.963     \\\\\n",
       "\\textbf{C(strike)[17]} &      23.2047  &        0.606     &    38.319  &         0.000        &       22.018    &       24.392     \\\\\n",
       "\\textbf{C(strike)[19]} &      22.0799  &        0.605     &    36.510  &         0.000        &       20.895    &       23.265     \\\\\n",
       "\\textbf{C(strike)[20]} &      22.9821  &        0.606     &    37.950  &         0.000        &       21.795    &       24.169     \\\\\n",
       "\\textbf{C(strike)[24]} &      40.5481  &        0.608     &    66.689  &         0.000        &       39.356    &       41.740     \\\\\n",
       "\\textbf{C(strike)[26]} &      24.0630  &        0.604     &    39.861  &         0.000        &       22.880    &       25.246     \\\\\n",
       "\\textbf{C(strike)[27]} &      22.4392  &        0.606     &    37.017  &         0.000        &       21.251    &       23.627     \\\\\n",
       "\\textbf{C(strike)[28]} &      23.5532  &        0.606     &    38.856  &         0.000        &       22.365    &       24.741     \\\\\n",
       "\\textbf{C(strike)[31]} &      18.2241  &        0.605     &    30.104  &         0.000        &       17.038    &       19.411     \\\\\n",
       "\\textbf{C(strike)[32]} &      31.9170  &        0.606     &    52.697  &         0.000        &       30.730    &       33.104     \\\\\n",
       "\\textbf{C(strike)[33]} &      24.0314  &        0.606     &    39.665  &         0.000        &       22.844    &       25.219     \\\\\n",
       "\\textbf{C(strike)[34]} &      16.9191  &        0.606     &    27.933  &         0.000        &       15.732    &       18.106     \\\\\n",
       "\\textbf{C(strike)[35]} &      16.9226  &        0.606     &    27.929  &         0.000        &       15.735    &       18.110     \\\\\n",
       "\\textbf{X\\_1}          &      -0.5667  &        1.763     &    -0.321  &         0.748        &       -4.022    &        2.889     \\\\\n",
       "\\textbf{X\\_2}          &       1.2592  &        1.270     &     0.992  &         0.321        &       -1.229    &        3.748     \\\\\n",
       "\\textbf{X\\_3}          &       0.3396  &        0.977     &     0.348  &         0.728        &       -1.575    &        2.254     \\\\\n",
       "\\textbf{X\\_4}          &      -0.0628  &        0.757     &    -0.083  &         0.934        &       -1.547    &        1.421     \\\\\n",
       "\\textbf{X\\_5}          &       0.4180  &        0.813     &     0.514  &         0.607        &       -1.176    &        2.012     \\\\\n",
       "\\textbf{X\\_7}          &      -0.1092  &        1.038     &    -0.105  &         0.916        &       -2.144    &        1.926     \\\\\n",
       "\\textbf{X\\_8}          &       2.6705  &        1.188     &     2.247  &         0.025        &        0.342    &        4.999     \\\\\n",
       "\\textbf{X\\_9}          &       2.6617  &        1.261     &     2.110  &         0.035        &        0.189    &        5.134     \\\\\n",
       "\\textbf{X\\_10}         &       2.5699  &        1.383     &     1.858  &         0.063        &       -0.141    &        5.281     \\\\\n",
       "\\textbf{X\\_11}         &       1.5476  &        0.731     &     2.117  &         0.034        &        0.115    &        2.980     \\\\\n",
       "\\textbf{X\\_12}         &       0.0056  &        0.704     &     0.008  &         0.994        &       -1.374    &        1.385     \\\\\n",
       "\\textbf{X\\_13}         &       0.8569  &        1.048     &     0.818  &         0.413        &       -1.197    &        2.911     \\\\\n",
       "\\textbf{X\\_14}         &       1.8323  &        1.232     &     1.487  &         0.137        &       -0.582    &        4.247     \\\\\n",
       "\\textbf{X\\_15}         &      -0.7909  &        1.112     &    -0.711  &         0.477        &       -2.971    &        1.389     \\\\\n",
       "\\textbf{X\\_16}         &       0.2300  &        0.688     &     0.334  &         0.738        &       -1.119    &        1.579     \\\\\n",
       "\\textbf{X\\_17}         &       0.8072  &        0.908     &     0.889  &         0.374        &       -0.973    &        2.587     \\\\\n",
       "\\textbf{X\\_18}         &       1.1577  &        0.892     &     1.297  &         0.194        &       -0.591    &        2.906     \\\\\n",
       "\\textbf{X\\_19}         &       0.2258  &        0.873     &     0.259  &         0.796        &       -1.486    &        1.937     \\\\\n",
       "\\textbf{X\\_20}         &       1.8895  &        0.991     &     1.906  &         0.057        &       -0.053    &        3.832     \\\\\n",
       "\\textbf{X\\_21}         &       2.1473  &        1.121     &     1.915  &         0.055        &       -0.050    &        4.345     \\\\\n",
       "\\textbf{X\\_22}         &       0.6174  &        1.237     &     0.499  &         0.618        &       -1.808    &        3.042     \\\\\n",
       "\\textbf{X\\_23}         &       1.5753  &        0.629     &     2.505  &         0.012        &        0.343    &        2.808     \\\\\n",
       "\\textbf{X\\_24}         &       1.5368  &        0.995     &     1.544  &         0.123        &       -0.414    &        3.487     \\\\\n",
       "\\textbf{X\\_25}         &       1.5337  &        1.016     &     1.510  &         0.131        &       -0.457    &        3.524     \\\\\n",
       "\\textbf{X\\_26}         &       0.1641  &        0.937     &     0.175  &         0.861        &       -1.672    &        2.000     \\\\\n",
       "\\textbf{X\\_27}         &      -0.1996  &        0.494     &    -0.404  &         0.686        &       -1.169    &        0.769     \\\\\n",
       "\\textbf{X\\_28}         &       2.3399  &        1.447     &     1.617  &         0.106        &       -0.497    &        5.177     \\\\\n",
       "\\textbf{X\\_29}         &      -0.4404  &        0.818     &    -0.539  &         0.590        &       -2.043    &        1.162     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 674661.180 & \\textbf{  Durbin-Watson:     } &      1.994    \\\\\n",
       "\\textbf{Prob(Omnibus):} &    0.000   & \\textbf{  Jarque-Bera (JB):  } & 39069914.316  \\\\\n",
       "\\textbf{Skew:}          &    4.513   & \\textbf{  Prob(JB):          } &       0.00    \\\\\n",
       "\\textbf{Kurtosis:}      &   38.065   & \\textbf{  Cond. No.          } &       16.1    \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors are robust to cluster correlation (cluster)"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:               mobility   R-squared:                       0.010\n",
       "Model:                            OLS   Adj. R-squared:                  0.009\n",
       "Method:                 Least Squares   F-statistic:                       nan\n",
       "Date:                Wed, 11 Jun 2025   Prob (F-statistic):                nan\n",
       "Time:                        14:33:30   Log-Likelihood:            -3.8240e+06\n",
       "No. Observations:              715218   AIC:                         7.648e+06\n",
       "Df Residuals:                  715165   BIC:                         7.649e+06\n",
       "Df Model:                          52                                         \n",
       "Covariance Type:              cluster                                         \n",
       "=================================================================================\n",
       "                    coef    std err          z      P>|z|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------\n",
       "C(strike)[1]     23.4435      0.606     38.717      0.000      22.257      24.630\n",
       "C(strike)[3]     24.5668      0.605     40.581      0.000      23.380      25.753\n",
       "C(strike)[4]     29.9910      0.604     49.617      0.000      28.806      31.176\n",
       "C(strike)[5]     29.0898      0.607     47.949      0.000      27.901      30.279\n",
       "C(strike)[6]     32.2995      0.606     53.277      0.000      31.111      33.488\n",
       "C(strike)[7]     27.5372      0.606     45.455      0.000      26.350      28.725\n",
       "C(strike)[8]     27.1364      0.605     44.837      0.000      25.950      28.323\n",
       "C(strike)[9]     18.6165      0.605     30.757      0.000      17.430      19.803\n",
       "C(strike)[10]    22.6344      0.606     37.364      0.000      21.447      23.822\n",
       "C(strike)[11]    31.4049      0.606     51.841      0.000      30.218      32.592\n",
       "C(strike)[12]    22.0904      0.604     36.551      0.000      20.906      23.275\n",
       "C(strike)[14]    16.2298      0.605     26.810      0.000      15.043      17.416\n",
       "C(strike)[15]    27.7744      0.606     45.811      0.000      26.586      28.963\n",
       "C(strike)[17]    23.2047      0.606     38.319      0.000      22.018      24.392\n",
       "C(strike)[19]    22.0799      0.605     36.510      0.000      20.895      23.265\n",
       "C(strike)[20]    22.9821      0.606     37.950      0.000      21.795      24.169\n",
       "C(strike)[24]    40.5481      0.608     66.689      0.000      39.356      41.740\n",
       "C(strike)[26]    24.0630      0.604     39.861      0.000      22.880      25.246\n",
       "C(strike)[27]    22.4392      0.606     37.017      0.000      21.251      23.627\n",
       "C(strike)[28]    23.5532      0.606     38.856      0.000      22.365      24.741\n",
       "C(strike)[31]    18.2241      0.605     30.104      0.000      17.038      19.411\n",
       "C(strike)[32]    31.9170      0.606     52.697      0.000      30.730      33.104\n",
       "C(strike)[33]    24.0314      0.606     39.665      0.000      22.844      25.219\n",
       "C(strike)[34]    16.9191      0.606     27.933      0.000      15.732      18.106\n",
       "C(strike)[35]    16.9226      0.606     27.929      0.000      15.735      18.110\n",
       "X_1              -0.5667      1.763     -0.321      0.748      -4.022       2.889\n",
       "X_2               1.2592      1.270      0.992      0.321      -1.229       3.748\n",
       "X_3               0.3396      0.977      0.348      0.728      -1.575       2.254\n",
       "X_4              -0.0628      0.757     -0.083      0.934      -1.547       1.421\n",
       "X_5               0.4180      0.813      0.514      0.607      -1.176       2.012\n",
       "X_7              -0.1092      1.038     -0.105      0.916      -2.144       1.926\n",
       "X_8               2.6705      1.188      2.247      0.025       0.342       4.999\n",
       "X_9               2.6617      1.261      2.110      0.035       0.189       5.134\n",
       "X_10              2.5699      1.383      1.858      0.063      -0.141       5.281\n",
       "X_11              1.5476      0.731      2.117      0.034       0.115       2.980\n",
       "X_12              0.0056      0.704      0.008      0.994      -1.374       1.385\n",
       "X_13              0.8569      1.048      0.818      0.413      -1.197       2.911\n",
       "X_14              1.8323      1.232      1.487      0.137      -0.582       4.247\n",
       "X_15             -0.7909      1.112     -0.711      0.477      -2.971       1.389\n",
       "X_16              0.2300      0.688      0.334      0.738      -1.119       1.579\n",
       "X_17              0.8072      0.908      0.889      0.374      -0.973       2.587\n",
       "X_18              1.1577      0.892      1.297      0.194      -0.591       2.906\n",
       "X_19              0.2258      0.873      0.259      0.796      -1.486       1.937\n",
       "X_20              1.8895      0.991      1.906      0.057      -0.053       3.832\n",
       "X_21              2.1473      1.121      1.915      0.055      -0.050       4.345\n",
       "X_22              0.6174      1.237      0.499      0.618      -1.808       3.042\n",
       "X_23              1.5753      0.629      2.505      0.012       0.343       2.808\n",
       "X_24              1.5368      0.995      1.544      0.123      -0.414       3.487\n",
       "X_25              1.5337      1.016      1.510      0.131      -0.457       3.524\n",
       "X_26              0.1641      0.937      0.175      0.861      -1.672       2.000\n",
       "X_27             -0.1996      0.494     -0.404      0.686      -1.169       0.769\n",
       "X_28              2.3399      1.447      1.617      0.106      -0.497       5.177\n",
       "X_29             -0.4404      0.818     -0.539      0.590      -2.043       1.162\n",
       "==============================================================================\n",
       "Omnibus:                   674661.180   Durbin-Watson:                   1.994\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):         39069914.316\n",
       "Skew:                           4.513   Prob(JB):                         0.00\n",
       "Kurtosis:                      38.065   Cond. No.                         16.1\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors are robust to cluster correlation (cluster)\n",
       "\"\"\""
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# run the event study regression for mobility around strikes\n",
    "# 7 lags and 21 leads\n",
    "# time vars provide the parameters of interest\n",
    "# drop the day = -2 variable for difference in means and to avoid multicollinearity\n",
    "# use strike fixed effects and cluster SE by strike\n",
    "\n",
    "reg = sm.ols('mobility ~ ' + var_form + ' +  C(strike) - 1', \n",
    "             data=df_period1).fit(cov_type='cluster',\n",
    "                          cov_kwds={'groups': df_period1['strike'].values})\n",
    "\n",
    "# save parameters and confidence intervals for plotting\n",
    "conf_int = reg.conf_int()\n",
    "res_period1 = pd.DataFrame({\n",
    "    'params': reg.params,\n",
    "    'ci_l': conf_int[0],  # lower bound\n",
    "    'ci_h': conf_int[1],  # upper bound\n",
    "})\n",
    "\n",
    "# print regression summary\n",
    "reg.summary()\n",
    "# reindex the results to go from day = -7 to day = 21"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>mobility</td>     <th>  R-squared:         </th>  <td>   0.013</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.013</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>     nan</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 11 Jun 2025</td> <th>  Prob (F-statistic):</th>   <td>   nan</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>14:33:51</td>     <th>  Log-Likelihood:    </th> <td>-4.7714e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>889341</td>      <th>  AIC:               </th>  <td>9.543e+06</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>889289</td>      <th>  BIC:               </th>  <td>9.544e+06</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    51</td>      <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>       <td>cluster</td>     <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td></td>           <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[36]</th> <td>   23.2290</td> <td>    0.847</td> <td>   27.434</td> <td> 0.000</td> <td>   21.569</td> <td>   24.889</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[37]</th> <td>   18.2497</td> <td>    0.849</td> <td>   21.500</td> <td> 0.000</td> <td>   16.586</td> <td>   19.913</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[38]</th> <td>   15.2801</td> <td>    0.845</td> <td>   18.082</td> <td> 0.000</td> <td>   13.624</td> <td>   16.936</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[39]</th> <td>   21.5523</td> <td>    0.849</td> <td>   25.384</td> <td> 0.000</td> <td>   19.888</td> <td>   23.216</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[40]</th> <td>   19.0403</td> <td>    0.845</td> <td>   22.533</td> <td> 0.000</td> <td>   17.384</td> <td>   20.696</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[41]</th> <td>   18.9778</td> <td>    0.847</td> <td>   22.417</td> <td> 0.000</td> <td>   17.319</td> <td>   20.637</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[42]</th> <td>   17.4108</td> <td>    0.844</td> <td>   20.630</td> <td> 0.000</td> <td>   15.757</td> <td>   19.065</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[43]</th> <td>   24.8263</td> <td>    0.848</td> <td>   29.278</td> <td> 0.000</td> <td>   23.164</td> <td>   26.488</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[45]</th> <td>   60.0600</td> <td>    0.848</td> <td>   70.829</td> <td> 0.000</td> <td>   58.398</td> <td>   61.722</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[47]</th> <td>   34.2941</td> <td>    0.844</td> <td>   40.622</td> <td> 0.000</td> <td>   32.639</td> <td>   35.949</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[48]</th> <td>   18.9554</td> <td>    0.843</td> <td>   22.473</td> <td> 0.000</td> <td>   17.302</td> <td>   20.609</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[49]</th> <td>   30.6826</td> <td>    0.847</td> <td>   36.221</td> <td> 0.000</td> <td>   29.022</td> <td>   32.343</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[50]</th> <td>   32.8393</td> <td>    0.847</td> <td>   38.762</td> <td> 0.000</td> <td>   31.179</td> <td>   34.500</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[51]</th> <td>   42.6701</td> <td>    0.845</td> <td>   50.498</td> <td> 0.000</td> <td>   41.014</td> <td>   44.326</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[54]</th> <td>   27.3745</td> <td>    0.848</td> <td>   32.283</td> <td> 0.000</td> <td>   25.713</td> <td>   29.036</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[56]</th> <td>   24.2665</td> <td>    0.845</td> <td>   28.730</td> <td> 0.000</td> <td>   22.611</td> <td>   25.922</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[57]</th> <td>   31.3674</td> <td>    0.848</td> <td>   36.998</td> <td> 0.000</td> <td>   29.706</td> <td>   33.029</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[58]</th> <td>   26.2223</td> <td>    0.842</td> <td>   31.127</td> <td> 0.000</td> <td>   24.571</td> <td>   27.873</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[59]</th> <td>   18.3042</td> <td>    0.846</td> <td>   21.628</td> <td> 0.000</td> <td>   16.645</td> <td>   19.963</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[60]</th> <td>   28.6205</td> <td>    0.839</td> <td>   34.122</td> <td> 0.000</td> <td>   26.977</td> <td>   30.264</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[61]</th> <td>   22.3968</td> <td>    0.837</td> <td>   26.744</td> <td> 0.000</td> <td>   20.755</td> <td>   24.038</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[62]</th> <td>   27.2765</td> <td>    0.843</td> <td>   32.340</td> <td> 0.000</td> <td>   25.623</td> <td>   28.930</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[64]</th> <td>   32.8794</td> <td>    0.847</td> <td>   38.835</td> <td> 0.000</td> <td>   31.220</td> <td>   34.539</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[65]</th> <td>   40.9580</td> <td>    0.844</td> <td>   48.539</td> <td> 0.000</td> <td>   39.304</td> <td>   42.612</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_1</th>           <td>    0.7657</td> <td>    0.939</td> <td>    0.815</td> <td> 0.415</td> <td>   -1.075</td> <td>    2.607</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_2</th>           <td>   -0.0964</td> <td>    1.386</td> <td>   -0.070</td> <td> 0.945</td> <td>   -2.814</td> <td>    2.621</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_3</th>           <td>   -0.3588</td> <td>    1.157</td> <td>   -0.310</td> <td> 0.757</td> <td>   -2.627</td> <td>    1.909</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_4</th>           <td>    1.1850</td> <td>    0.982</td> <td>    1.207</td> <td> 0.227</td> <td>   -0.739</td> <td>    3.109</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_5</th>           <td>    0.8831</td> <td>    0.979</td> <td>    0.902</td> <td> 0.367</td> <td>   -1.036</td> <td>    2.802</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_7</th>           <td>    1.1815</td> <td>    1.041</td> <td>    1.135</td> <td> 0.256</td> <td>   -0.858</td> <td>    3.221</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_8</th>           <td>    7.5501</td> <td>    1.562</td> <td>    4.834</td> <td> 0.000</td> <td>    4.489</td> <td>   10.611</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_9</th>           <td>    3.2285</td> <td>    1.223</td> <td>    2.641</td> <td> 0.008</td> <td>    0.832</td> <td>    5.625</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_10</th>          <td>    2.3418</td> <td>    1.185</td> <td>    1.976</td> <td> 0.048</td> <td>    0.019</td> <td>    4.665</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_11</th>          <td>    2.4134</td> <td>    0.924</td> <td>    2.611</td> <td> 0.009</td> <td>    0.602</td> <td>    4.225</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_12</th>          <td>    0.8540</td> <td>    1.335</td> <td>    0.640</td> <td> 0.522</td> <td>   -1.762</td> <td>    3.470</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_13</th>          <td>    0.8110</td> <td>    0.400</td> <td>    2.027</td> <td> 0.043</td> <td>    0.027</td> <td>    1.595</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_14</th>          <td>    2.1938</td> <td>    1.053</td> <td>    2.083</td> <td> 0.037</td> <td>    0.130</td> <td>    4.258</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_15</th>          <td>    1.6615</td> <td>    0.995</td> <td>    1.669</td> <td> 0.095</td> <td>   -0.289</td> <td>    3.612</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_16</th>          <td>    2.4981</td> <td>    1.207</td> <td>    2.070</td> <td> 0.038</td> <td>    0.133</td> <td>    4.863</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_17</th>          <td>    2.4096</td> <td>    1.095</td> <td>    2.200</td> <td> 0.028</td> <td>    0.263</td> <td>    4.556</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_18</th>          <td>    2.6986</td> <td>    1.403</td> <td>    1.923</td> <td> 0.054</td> <td>   -0.051</td> <td>    5.448</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_19</th>          <td>    1.2073</td> <td>    1.260</td> <td>    0.958</td> <td> 0.338</td> <td>   -1.262</td> <td>    3.676</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_20</th>          <td>    1.1857</td> <td>    0.948</td> <td>    1.250</td> <td> 0.211</td> <td>   -0.673</td> <td>    3.045</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_21</th>          <td>    2.5603</td> <td>    1.258</td> <td>    2.036</td> <td> 0.042</td> <td>    0.096</td> <td>    5.025</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_22</th>          <td>    1.3066</td> <td>    1.237</td> <td>    1.056</td> <td> 0.291</td> <td>   -1.118</td> <td>    3.732</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_23</th>          <td>    1.2311</td> <td>    1.325</td> <td>    0.929</td> <td> 0.353</td> <td>   -1.366</td> <td>    3.828</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_24</th>          <td>    1.8609</td> <td>    1.151</td> <td>    1.616</td> <td> 0.106</td> <td>   -0.396</td> <td>    4.117</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_25</th>          <td>    2.3539</td> <td>    1.267</td> <td>    1.859</td> <td> 0.063</td> <td>   -0.128</td> <td>    4.836</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_26</th>          <td>    1.4618</td> <td>    1.400</td> <td>    1.044</td> <td> 0.296</td> <td>   -1.281</td> <td>    4.205</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_27</th>          <td>    0.2012</td> <td>    1.229</td> <td>    0.164</td> <td> 0.870</td> <td>   -2.208</td> <td>    2.610</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_28</th>          <td>    1.6612</td> <td>    1.431</td> <td>    1.161</td> <td> 0.246</td> <td>   -1.143</td> <td>    4.465</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_29</th>          <td>    1.1215</td> <td>    1.055</td> <td>    1.063</td> <td> 0.288</td> <td>   -0.946</td> <td>    3.189</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>1000990.130</td> <th>  Durbin-Watson:     </th>   <td>   1.985</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>   <td> 0.000</td>    <th>  Jarque-Bera (JB):  </th> <td>197745322.169</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>            <td> 5.582</td>    <th>  Prob(JB):          </th>   <td>    0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>        <td>75.193</td>    <th>  Cond. No.          </th>   <td>    22.0</td>   \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors are robust to cluster correlation (cluster)"
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &     mobility     & \\textbf{  R-squared:         } &       0.013    \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &       0.013    \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &         nan    \\\\\n",
       "\\textbf{Date:}             & Wed, 11 Jun 2025 & \\textbf{  Prob (F-statistic):} &        nan     \\\\\n",
       "\\textbf{Time:}             &     14:33:51     & \\textbf{  Log-Likelihood:    } &  -4.7714e+06   \\\\\n",
       "\\textbf{No. Observations:} &      889341      & \\textbf{  AIC:               } &   9.543e+06    \\\\\n",
       "\\textbf{Df Residuals:}     &      889289      & \\textbf{  BIC:               } &   9.544e+06    \\\\\n",
       "\\textbf{Df Model:}         &          51      & \\textbf{                     } &                \\\\\n",
       "\\textbf{Covariance Type:}  &     cluster      & \\textbf{                     } &                \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                       & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{C(strike)[36]} &      23.2290  &        0.847     &    27.434  &         0.000        &       21.569    &       24.889     \\\\\n",
       "\\textbf{C(strike)[37]} &      18.2497  &        0.849     &    21.500  &         0.000        &       16.586    &       19.913     \\\\\n",
       "\\textbf{C(strike)[38]} &      15.2801  &        0.845     &    18.082  &         0.000        &       13.624    &       16.936     \\\\\n",
       "\\textbf{C(strike)[39]} &      21.5523  &        0.849     &    25.384  &         0.000        &       19.888    &       23.216     \\\\\n",
       "\\textbf{C(strike)[40]} &      19.0403  &        0.845     &    22.533  &         0.000        &       17.384    &       20.696     \\\\\n",
       "\\textbf{C(strike)[41]} &      18.9778  &        0.847     &    22.417  &         0.000        &       17.319    &       20.637     \\\\\n",
       "\\textbf{C(strike)[42]} &      17.4108  &        0.844     &    20.630  &         0.000        &       15.757    &       19.065     \\\\\n",
       "\\textbf{C(strike)[43]} &      24.8263  &        0.848     &    29.278  &         0.000        &       23.164    &       26.488     \\\\\n",
       "\\textbf{C(strike)[45]} &      60.0600  &        0.848     &    70.829  &         0.000        &       58.398    &       61.722     \\\\\n",
       "\\textbf{C(strike)[47]} &      34.2941  &        0.844     &    40.622  &         0.000        &       32.639    &       35.949     \\\\\n",
       "\\textbf{C(strike)[48]} &      18.9554  &        0.843     &    22.473  &         0.000        &       17.302    &       20.609     \\\\\n",
       "\\textbf{C(strike)[49]} &      30.6826  &        0.847     &    36.221  &         0.000        &       29.022    &       32.343     \\\\\n",
       "\\textbf{C(strike)[50]} &      32.8393  &        0.847     &    38.762  &         0.000        &       31.179    &       34.500     \\\\\n",
       "\\textbf{C(strike)[51]} &      42.6701  &        0.845     &    50.498  &         0.000        &       41.014    &       44.326     \\\\\n",
       "\\textbf{C(strike)[54]} &      27.3745  &        0.848     &    32.283  &         0.000        &       25.713    &       29.036     \\\\\n",
       "\\textbf{C(strike)[56]} &      24.2665  &        0.845     &    28.730  &         0.000        &       22.611    &       25.922     \\\\\n",
       "\\textbf{C(strike)[57]} &      31.3674  &        0.848     &    36.998  &         0.000        &       29.706    &       33.029     \\\\\n",
       "\\textbf{C(strike)[58]} &      26.2223  &        0.842     &    31.127  &         0.000        &       24.571    &       27.873     \\\\\n",
       "\\textbf{C(strike)[59]} &      18.3042  &        0.846     &    21.628  &         0.000        &       16.645    &       19.963     \\\\\n",
       "\\textbf{C(strike)[60]} &      28.6205  &        0.839     &    34.122  &         0.000        &       26.977    &       30.264     \\\\\n",
       "\\textbf{C(strike)[61]} &      22.3968  &        0.837     &    26.744  &         0.000        &       20.755    &       24.038     \\\\\n",
       "\\textbf{C(strike)[62]} &      27.2765  &        0.843     &    32.340  &         0.000        &       25.623    &       28.930     \\\\\n",
       "\\textbf{C(strike)[64]} &      32.8794  &        0.847     &    38.835  &         0.000        &       31.220    &       34.539     \\\\\n",
       "\\textbf{C(strike)[65]} &      40.9580  &        0.844     &    48.539  &         0.000        &       39.304    &       42.612     \\\\\n",
       "\\textbf{X\\_1}          &       0.7657  &        0.939     &     0.815  &         0.415        &       -1.075    &        2.607     \\\\\n",
       "\\textbf{X\\_2}          &      -0.0964  &        1.386     &    -0.070  &         0.945        &       -2.814    &        2.621     \\\\\n",
       "\\textbf{X\\_3}          &      -0.3588  &        1.157     &    -0.310  &         0.757        &       -2.627    &        1.909     \\\\\n",
       "\\textbf{X\\_4}          &       1.1850  &        0.982     &     1.207  &         0.227        &       -0.739    &        3.109     \\\\\n",
       "\\textbf{X\\_5}          &       0.8831  &        0.979     &     0.902  &         0.367        &       -1.036    &        2.802     \\\\\n",
       "\\textbf{X\\_7}          &       1.1815  &        1.041     &     1.135  &         0.256        &       -0.858    &        3.221     \\\\\n",
       "\\textbf{X\\_8}          &       7.5501  &        1.562     &     4.834  &         0.000        &        4.489    &       10.611     \\\\\n",
       "\\textbf{X\\_9}          &       3.2285  &        1.223     &     2.641  &         0.008        &        0.832    &        5.625     \\\\\n",
       "\\textbf{X\\_10}         &       2.3418  &        1.185     &     1.976  &         0.048        &        0.019    &        4.665     \\\\\n",
       "\\textbf{X\\_11}         &       2.4134  &        0.924     &     2.611  &         0.009        &        0.602    &        4.225     \\\\\n",
       "\\textbf{X\\_12}         &       0.8540  &        1.335     &     0.640  &         0.522        &       -1.762    &        3.470     \\\\\n",
       "\\textbf{X\\_13}         &       0.8110  &        0.400     &     2.027  &         0.043        &        0.027    &        1.595     \\\\\n",
       "\\textbf{X\\_14}         &       2.1938  &        1.053     &     2.083  &         0.037        &        0.130    &        4.258     \\\\\n",
       "\\textbf{X\\_15}         &       1.6615  &        0.995     &     1.669  &         0.095        &       -0.289    &        3.612     \\\\\n",
       "\\textbf{X\\_16}         &       2.4981  &        1.207     &     2.070  &         0.038        &        0.133    &        4.863     \\\\\n",
       "\\textbf{X\\_17}         &       2.4096  &        1.095     &     2.200  &         0.028        &        0.263    &        4.556     \\\\\n",
       "\\textbf{X\\_18}         &       2.6986  &        1.403     &     1.923  &         0.054        &       -0.051    &        5.448     \\\\\n",
       "\\textbf{X\\_19}         &       1.2073  &        1.260     &     0.958  &         0.338        &       -1.262    &        3.676     \\\\\n",
       "\\textbf{X\\_20}         &       1.1857  &        0.948     &     1.250  &         0.211        &       -0.673    &        3.045     \\\\\n",
       "\\textbf{X\\_21}         &       2.5603  &        1.258     &     2.036  &         0.042        &        0.096    &        5.025     \\\\\n",
       "\\textbf{X\\_22}         &       1.3066  &        1.237     &     1.056  &         0.291        &       -1.118    &        3.732     \\\\\n",
       "\\textbf{X\\_23}         &       1.2311  &        1.325     &     0.929  &         0.353        &       -1.366    &        3.828     \\\\\n",
       "\\textbf{X\\_24}         &       1.8609  &        1.151     &     1.616  &         0.106        &       -0.396    &        4.117     \\\\\n",
       "\\textbf{X\\_25}         &       2.3539  &        1.267     &     1.859  &         0.063        &       -0.128    &        4.836     \\\\\n",
       "\\textbf{X\\_26}         &       1.4618  &        1.400     &     1.044  &         0.296        &       -1.281    &        4.205     \\\\\n",
       "\\textbf{X\\_27}         &       0.2012  &        1.229     &     0.164  &         0.870        &       -2.208    &        2.610     \\\\\n",
       "\\textbf{X\\_28}         &       1.6612  &        1.431     &     1.161  &         0.246        &       -1.143    &        4.465     \\\\\n",
       "\\textbf{X\\_29}         &       1.1215  &        1.055     &     1.063  &         0.288        &       -0.946    &        3.189     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 1000990.130 & \\textbf{  Durbin-Watson:     } &       1.985    \\\\\n",
       "\\textbf{Prob(Omnibus):} &     0.000   & \\textbf{  Jarque-Bera (JB):  } & 197745322.169  \\\\\n",
       "\\textbf{Skew:}          &     5.582   & \\textbf{  Prob(JB):          } &        0.00    \\\\\n",
       "\\textbf{Kurtosis:}      &    75.193   & \\textbf{  Cond. No.          } &        22.0    \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors are robust to cluster correlation (cluster)"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:               mobility   R-squared:                       0.013\n",
       "Model:                            OLS   Adj. R-squared:                  0.013\n",
       "Method:                 Least Squares   F-statistic:                       nan\n",
       "Date:                Wed, 11 Jun 2025   Prob (F-statistic):                nan\n",
       "Time:                        14:33:51   Log-Likelihood:            -4.7714e+06\n",
       "No. Observations:              889341   AIC:                         9.543e+06\n",
       "Df Residuals:                  889289   BIC:                         9.544e+06\n",
       "Df Model:                          51                                         \n",
       "Covariance Type:              cluster                                         \n",
       "=================================================================================\n",
       "                    coef    std err          z      P>|z|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------\n",
       "C(strike)[36]    23.2290      0.847     27.434      0.000      21.569      24.889\n",
       "C(strike)[37]    18.2497      0.849     21.500      0.000      16.586      19.913\n",
       "C(strike)[38]    15.2801      0.845     18.082      0.000      13.624      16.936\n",
       "C(strike)[39]    21.5523      0.849     25.384      0.000      19.888      23.216\n",
       "C(strike)[40]    19.0403      0.845     22.533      0.000      17.384      20.696\n",
       "C(strike)[41]    18.9778      0.847     22.417      0.000      17.319      20.637\n",
       "C(strike)[42]    17.4108      0.844     20.630      0.000      15.757      19.065\n",
       "C(strike)[43]    24.8263      0.848     29.278      0.000      23.164      26.488\n",
       "C(strike)[45]    60.0600      0.848     70.829      0.000      58.398      61.722\n",
       "C(strike)[47]    34.2941      0.844     40.622      0.000      32.639      35.949\n",
       "C(strike)[48]    18.9554      0.843     22.473      0.000      17.302      20.609\n",
       "C(strike)[49]    30.6826      0.847     36.221      0.000      29.022      32.343\n",
       "C(strike)[50]    32.8393      0.847     38.762      0.000      31.179      34.500\n",
       "C(strike)[51]    42.6701      0.845     50.498      0.000      41.014      44.326\n",
       "C(strike)[54]    27.3745      0.848     32.283      0.000      25.713      29.036\n",
       "C(strike)[56]    24.2665      0.845     28.730      0.000      22.611      25.922\n",
       "C(strike)[57]    31.3674      0.848     36.998      0.000      29.706      33.029\n",
       "C(strike)[58]    26.2223      0.842     31.127      0.000      24.571      27.873\n",
       "C(strike)[59]    18.3042      0.846     21.628      0.000      16.645      19.963\n",
       "C(strike)[60]    28.6205      0.839     34.122      0.000      26.977      30.264\n",
       "C(strike)[61]    22.3968      0.837     26.744      0.000      20.755      24.038\n",
       "C(strike)[62]    27.2765      0.843     32.340      0.000      25.623      28.930\n",
       "C(strike)[64]    32.8794      0.847     38.835      0.000      31.220      34.539\n",
       "C(strike)[65]    40.9580      0.844     48.539      0.000      39.304      42.612\n",
       "X_1               0.7657      0.939      0.815      0.415      -1.075       2.607\n",
       "X_2              -0.0964      1.386     -0.070      0.945      -2.814       2.621\n",
       "X_3              -0.3588      1.157     -0.310      0.757      -2.627       1.909\n",
       "X_4               1.1850      0.982      1.207      0.227      -0.739       3.109\n",
       "X_5               0.8831      0.979      0.902      0.367      -1.036       2.802\n",
       "X_7               1.1815      1.041      1.135      0.256      -0.858       3.221\n",
       "X_8               7.5501      1.562      4.834      0.000       4.489      10.611\n",
       "X_9               3.2285      1.223      2.641      0.008       0.832       5.625\n",
       "X_10              2.3418      1.185      1.976      0.048       0.019       4.665\n",
       "X_11              2.4134      0.924      2.611      0.009       0.602       4.225\n",
       "X_12              0.8540      1.335      0.640      0.522      -1.762       3.470\n",
       "X_13              0.8110      0.400      2.027      0.043       0.027       1.595\n",
       "X_14              2.1938      1.053      2.083      0.037       0.130       4.258\n",
       "X_15              1.6615      0.995      1.669      0.095      -0.289       3.612\n",
       "X_16              2.4981      1.207      2.070      0.038       0.133       4.863\n",
       "X_17              2.4096      1.095      2.200      0.028       0.263       4.556\n",
       "X_18              2.6986      1.403      1.923      0.054      -0.051       5.448\n",
       "X_19              1.2073      1.260      0.958      0.338      -1.262       3.676\n",
       "X_20              1.1857      0.948      1.250      0.211      -0.673       3.045\n",
       "X_21              2.5603      1.258      2.036      0.042       0.096       5.025\n",
       "X_22              1.3066      1.237      1.056      0.291      -1.118       3.732\n",
       "X_23              1.2311      1.325      0.929      0.353      -1.366       3.828\n",
       "X_24              1.8609      1.151      1.616      0.106      -0.396       4.117\n",
       "X_25              2.3539      1.267      1.859      0.063      -0.128       4.836\n",
       "X_26              1.4618      1.400      1.044      0.296      -1.281       4.205\n",
       "X_27              0.2012      1.229      0.164      0.870      -2.208       2.610\n",
       "X_28              1.6612      1.431      1.161      0.246      -1.143       4.465\n",
       "X_29              1.1215      1.055      1.063      0.288      -0.946       3.189\n",
       "==============================================================================\n",
       "Omnibus:                  1000990.130   Durbin-Watson:                   1.985\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):        197745322.169\n",
       "Skew:                           5.582   Prob(JB):                         0.00\n",
       "Kurtosis:                      75.193   Cond. No.                         22.0\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors are robust to cluster correlation (cluster)\n",
       "\"\"\""
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# run the event study regression for mobility around strikes\n",
    "# 7 lags and 21 leads\n",
    "# time vars provide the parameters of interest\n",
    "# drop the day = -2 variable for difference in means and to avoid multicollinearity\n",
    "# use strike fixed effects and cluster SE by strike\n",
    "\n",
    "reg = sm.ols('mobility ~ ' + var_form + ' +  C(strike) - 1', \n",
    "             data=df_period2).fit(cov_type='cluster',\n",
    "                          cov_kwds={'groups': df_period2['strike'].values})\n",
    "\n",
    "# save parameters and confidence intervals for plotting\n",
    "conf_int = reg.conf_int()\n",
    "res_period2 = pd.DataFrame({\n",
    "    'params': reg.params,\n",
    "    'ci_l': conf_int[0],  # lower bound\n",
    "    'ci_h': conf_int[1],  # upper bound\n",
    "})\n",
    "\n",
    "# print regression summary\n",
    "reg.summary()\n",
    "# reindex the results to go from day = -7 to day = 21"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>mobility</td>     <th>  R-squared:         </th>  <td>   0.021</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.021</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>     nan</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 11 Jun 2025</td> <th>  Prob (F-statistic):</th>   <td>   nan</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>14:34:20</td>     <th>  Log-Likelihood:    </th> <td>-1.0633e+07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>1776075</td>     <th>  AIC:               </th>  <td>2.127e+07</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>1776022</td>     <th>  BIC:               </th>  <td>2.127e+07</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    52</td>      <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>       <td>cluster</td>     <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>           <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[67]</th>  <td>   29.2688</td> <td>    0.682</td> <td>   42.930</td> <td> 0.000</td> <td>   27.933</td> <td>   30.605</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[68]</th>  <td>   27.4778</td> <td>    0.677</td> <td>   40.612</td> <td> 0.000</td> <td>   26.152</td> <td>   28.804</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[69]</th>  <td>   32.8900</td> <td>    0.703</td> <td>   46.786</td> <td> 0.000</td> <td>   31.512</td> <td>   34.268</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[70]</th>  <td>   23.4764</td> <td>    0.723</td> <td>   32.481</td> <td> 0.000</td> <td>   22.060</td> <td>   24.893</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[71]</th>  <td>   31.9136</td> <td>    0.703</td> <td>   45.388</td> <td> 0.000</td> <td>   30.535</td> <td>   33.292</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[72]</th>  <td>   33.9907</td> <td>    0.710</td> <td>   47.897</td> <td> 0.000</td> <td>   32.600</td> <td>   35.382</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[75]</th>  <td>   39.5380</td> <td>    0.712</td> <td>   55.552</td> <td> 0.000</td> <td>   38.143</td> <td>   40.933</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[76]</th>  <td>   61.8580</td> <td>    0.714</td> <td>   86.657</td> <td> 0.000</td> <td>   60.459</td> <td>   63.257</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[77]</th>  <td>   30.7782</td> <td>    0.712</td> <td>   43.212</td> <td> 0.000</td> <td>   29.382</td> <td>   32.174</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[78]</th>  <td>   26.9157</td> <td>    0.716</td> <td>   37.604</td> <td> 0.000</td> <td>   25.513</td> <td>   28.319</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[81]</th>  <td>   38.9212</td> <td>    0.700</td> <td>   55.632</td> <td> 0.000</td> <td>   37.550</td> <td>   40.292</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[82]</th>  <td>   26.8207</td> <td>    0.706</td> <td>   38.000</td> <td> 0.000</td> <td>   25.437</td> <td>   28.204</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[83]</th>  <td>   27.2563</td> <td>    0.704</td> <td>   38.738</td> <td> 0.000</td> <td>   25.877</td> <td>   28.635</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[85]</th>  <td>   25.9158</td> <td>    0.701</td> <td>   36.958</td> <td> 0.000</td> <td>   24.541</td> <td>   27.290</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[87]</th>  <td>   19.6960</td> <td>    0.711</td> <td>   27.705</td> <td> 0.000</td> <td>   18.303</td> <td>   21.089</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[90]</th>  <td>   14.4516</td> <td>    0.706</td> <td>   20.470</td> <td> 0.000</td> <td>   13.068</td> <td>   15.835</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[92]</th>  <td>   15.2271</td> <td>    0.710</td> <td>   21.440</td> <td> 0.000</td> <td>   13.835</td> <td>   16.619</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[95]</th>  <td>   18.8967</td> <td>    0.726</td> <td>   26.021</td> <td> 0.000</td> <td>   17.473</td> <td>   20.320</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[96]</th>  <td>   39.9156</td> <td>    0.710</td> <td>   56.217</td> <td> 0.000</td> <td>   38.524</td> <td>   41.307</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[97]</th>  <td>   13.6752</td> <td>    0.713</td> <td>   19.192</td> <td> 0.000</td> <td>   12.279</td> <td>   15.072</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[100]</th> <td>   28.9422</td> <td>    0.707</td> <td>   40.936</td> <td> 0.000</td> <td>   27.556</td> <td>   30.328</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[101]</th> <td>   43.7898</td> <td>    0.706</td> <td>   62.024</td> <td> 0.000</td> <td>   42.406</td> <td>   45.174</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[102]</th> <td>   33.8561</td> <td>    0.709</td> <td>   47.731</td> <td> 0.000</td> <td>   32.466</td> <td>   35.246</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[106]</th> <td>   23.4448</td> <td>    0.654</td> <td>   35.843</td> <td> 0.000</td> <td>   22.163</td> <td>   24.727</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[107]</th> <td>   31.7199</td> <td>    0.652</td> <td>   48.671</td> <td> 0.000</td> <td>   30.443</td> <td>   32.997</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_1</th>            <td>   -2.8660</td> <td>    3.223</td> <td>   -0.889</td> <td> 0.374</td> <td>   -9.184</td> <td>    3.452</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_2</th>            <td>   -3.0150</td> <td>    3.509</td> <td>   -0.859</td> <td> 0.390</td> <td>   -9.893</td> <td>    3.863</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_3</th>            <td>   -1.8165</td> <td>    3.342</td> <td>   -0.543</td> <td> 0.587</td> <td>   -8.367</td> <td>    4.734</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_4</th>            <td>   -0.1910</td> <td>    2.007</td> <td>   -0.095</td> <td> 0.924</td> <td>   -4.125</td> <td>    3.743</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_5</th>            <td>    0.5259</td> <td>    0.895</td> <td>    0.588</td> <td> 0.557</td> <td>   -1.228</td> <td>    2.280</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_7</th>            <td>   -2.3105</td> <td>    3.481</td> <td>   -0.664</td> <td> 0.507</td> <td>   -9.134</td> <td>    4.513</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_8</th>            <td>    7.4781</td> <td>    2.258</td> <td>    3.312</td> <td> 0.001</td> <td>    3.052</td> <td>   11.904</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_9</th>            <td>    3.8586</td> <td>    1.836</td> <td>    2.101</td> <td> 0.036</td> <td>    0.259</td> <td>    7.458</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_10</th>           <td>    3.2644</td> <td>    1.772</td> <td>    1.843</td> <td> 0.065</td> <td>   -0.208</td> <td>    6.737</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_11</th>           <td>    5.3296</td> <td>    3.152</td> <td>    1.691</td> <td> 0.091</td> <td>   -0.848</td> <td>   11.507</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_12</th>           <td>    0.7504</td> <td>    1.161</td> <td>    0.646</td> <td> 0.518</td> <td>   -1.525</td> <td>    3.026</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_13</th>           <td>    1.7368</td> <td>    0.936</td> <td>    1.855</td> <td> 0.064</td> <td>   -0.098</td> <td>    3.571</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_14</th>           <td>    1.8819</td> <td>    1.258</td> <td>    1.497</td> <td> 0.135</td> <td>   -0.583</td> <td>    4.347</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_15</th>           <td>    3.2434</td> <td>    2.083</td> <td>    1.557</td> <td> 0.120</td> <td>   -0.840</td> <td>    7.327</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_16</th>           <td>    3.8807</td> <td>    2.369</td> <td>    1.638</td> <td> 0.101</td> <td>   -0.763</td> <td>    8.524</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_17</th>           <td>    3.1145</td> <td>    2.067</td> <td>    1.507</td> <td> 0.132</td> <td>   -0.937</td> <td>    7.166</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_18</th>           <td>    2.1760</td> <td>    1.050</td> <td>    2.072</td> <td> 0.038</td> <td>    0.118</td> <td>    4.234</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_19</th>           <td>    1.1250</td> <td>    1.279</td> <td>    0.880</td> <td> 0.379</td> <td>   -1.381</td> <td>    3.631</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_20</th>           <td>    2.5944</td> <td>    1.731</td> <td>    1.499</td> <td> 0.134</td> <td>   -0.798</td> <td>    5.987</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_21</th>           <td>    4.4446</td> <td>    1.968</td> <td>    2.258</td> <td> 0.024</td> <td>    0.587</td> <td>    8.302</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_22</th>           <td>    3.4897</td> <td>    1.357</td> <td>    2.572</td> <td> 0.010</td> <td>    0.831</td> <td>    6.149</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_23</th>           <td>    3.5801</td> <td>    1.542</td> <td>    2.321</td> <td> 0.020</td> <td>    0.557</td> <td>    6.603</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_24</th>           <td>    3.1724</td> <td>    1.428</td> <td>    2.221</td> <td> 0.026</td> <td>    0.373</td> <td>    5.972</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_25</th>           <td>    2.8846</td> <td>    1.214</td> <td>    2.375</td> <td> 0.018</td> <td>    0.504</td> <td>    5.265</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_26</th>           <td>    1.4874</td> <td>    0.860</td> <td>    1.729</td> <td> 0.084</td> <td>   -0.199</td> <td>    3.174</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_27</th>           <td>    3.2482</td> <td>    1.753</td> <td>    1.853</td> <td> 0.064</td> <td>   -0.187</td> <td>    6.684</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_28</th>           <td>    3.8579</td> <td>    1.620</td> <td>    2.381</td> <td> 0.017</td> <td>    0.683</td> <td>    7.033</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_29</th>           <td>    4.3395</td> <td>    1.113</td> <td>    3.900</td> <td> 0.000</td> <td>    2.159</td> <td>    6.520</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>3424540.174</td> <th>  Durbin-Watson:     </th>    <td>   1.982</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>   <td> 0.000</td>    <th>  Jarque-Bera (JB):  </th> <td>10925565598.826</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>            <td>15.080</td>    <th>  Prob(JB):          </th>    <td>    0.00</td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>        <td>386.049</td>   <th>  Cond. No.          </th>    <td>    19.4</td>    \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors are robust to cluster correlation (cluster)"
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &     mobility     & \\textbf{  R-squared:         } &        0.021     \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &        0.021     \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &          nan     \\\\\n",
       "\\textbf{Date:}             & Wed, 11 Jun 2025 & \\textbf{  Prob (F-statistic):} &         nan      \\\\\n",
       "\\textbf{Time:}             &     14:34:20     & \\textbf{  Log-Likelihood:    } &   -1.0633e+07    \\\\\n",
       "\\textbf{No. Observations:} &     1776075      & \\textbf{  AIC:               } &    2.127e+07     \\\\\n",
       "\\textbf{Df Residuals:}     &     1776022      & \\textbf{  BIC:               } &    2.127e+07     \\\\\n",
       "\\textbf{Df Model:}         &          52      & \\textbf{                     } &                  \\\\\n",
       "\\textbf{Covariance Type:}  &     cluster      & \\textbf{                     } &                  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                        & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{C(strike)[67]}  &      29.2688  &        0.682     &    42.930  &         0.000        &       27.933    &       30.605     \\\\\n",
       "\\textbf{C(strike)[68]}  &      27.4778  &        0.677     &    40.612  &         0.000        &       26.152    &       28.804     \\\\\n",
       "\\textbf{C(strike)[69]}  &      32.8900  &        0.703     &    46.786  &         0.000        &       31.512    &       34.268     \\\\\n",
       "\\textbf{C(strike)[70]}  &      23.4764  &        0.723     &    32.481  &         0.000        &       22.060    &       24.893     \\\\\n",
       "\\textbf{C(strike)[71]}  &      31.9136  &        0.703     &    45.388  &         0.000        &       30.535    &       33.292     \\\\\n",
       "\\textbf{C(strike)[72]}  &      33.9907  &        0.710     &    47.897  &         0.000        &       32.600    &       35.382     \\\\\n",
       "\\textbf{C(strike)[75]}  &      39.5380  &        0.712     &    55.552  &         0.000        &       38.143    &       40.933     \\\\\n",
       "\\textbf{C(strike)[76]}  &      61.8580  &        0.714     &    86.657  &         0.000        &       60.459    &       63.257     \\\\\n",
       "\\textbf{C(strike)[77]}  &      30.7782  &        0.712     &    43.212  &         0.000        &       29.382    &       32.174     \\\\\n",
       "\\textbf{C(strike)[78]}  &      26.9157  &        0.716     &    37.604  &         0.000        &       25.513    &       28.319     \\\\\n",
       "\\textbf{C(strike)[81]}  &      38.9212  &        0.700     &    55.632  &         0.000        &       37.550    &       40.292     \\\\\n",
       "\\textbf{C(strike)[82]}  &      26.8207  &        0.706     &    38.000  &         0.000        &       25.437    &       28.204     \\\\\n",
       "\\textbf{C(strike)[83]}  &      27.2563  &        0.704     &    38.738  &         0.000        &       25.877    &       28.635     \\\\\n",
       "\\textbf{C(strike)[85]}  &      25.9158  &        0.701     &    36.958  &         0.000        &       24.541    &       27.290     \\\\\n",
       "\\textbf{C(strike)[87]}  &      19.6960  &        0.711     &    27.705  &         0.000        &       18.303    &       21.089     \\\\\n",
       "\\textbf{C(strike)[90]}  &      14.4516  &        0.706     &    20.470  &         0.000        &       13.068    &       15.835     \\\\\n",
       "\\textbf{C(strike)[92]}  &      15.2271  &        0.710     &    21.440  &         0.000        &       13.835    &       16.619     \\\\\n",
       "\\textbf{C(strike)[95]}  &      18.8967  &        0.726     &    26.021  &         0.000        &       17.473    &       20.320     \\\\\n",
       "\\textbf{C(strike)[96]}  &      39.9156  &        0.710     &    56.217  &         0.000        &       38.524    &       41.307     \\\\\n",
       "\\textbf{C(strike)[97]}  &      13.6752  &        0.713     &    19.192  &         0.000        &       12.279    &       15.072     \\\\\n",
       "\\textbf{C(strike)[100]} &      28.9422  &        0.707     &    40.936  &         0.000        &       27.556    &       30.328     \\\\\n",
       "\\textbf{C(strike)[101]} &      43.7898  &        0.706     &    62.024  &         0.000        &       42.406    &       45.174     \\\\\n",
       "\\textbf{C(strike)[102]} &      33.8561  &        0.709     &    47.731  &         0.000        &       32.466    &       35.246     \\\\\n",
       "\\textbf{C(strike)[106]} &      23.4448  &        0.654     &    35.843  &         0.000        &       22.163    &       24.727     \\\\\n",
       "\\textbf{C(strike)[107]} &      31.7199  &        0.652     &    48.671  &         0.000        &       30.443    &       32.997     \\\\\n",
       "\\textbf{X\\_1}           &      -2.8660  &        3.223     &    -0.889  &         0.374        &       -9.184    &        3.452     \\\\\n",
       "\\textbf{X\\_2}           &      -3.0150  &        3.509     &    -0.859  &         0.390        &       -9.893    &        3.863     \\\\\n",
       "\\textbf{X\\_3}           &      -1.8165  &        3.342     &    -0.543  &         0.587        &       -8.367    &        4.734     \\\\\n",
       "\\textbf{X\\_4}           &      -0.1910  &        2.007     &    -0.095  &         0.924        &       -4.125    &        3.743     \\\\\n",
       "\\textbf{X\\_5}           &       0.5259  &        0.895     &     0.588  &         0.557        &       -1.228    &        2.280     \\\\\n",
       "\\textbf{X\\_7}           &      -2.3105  &        3.481     &    -0.664  &         0.507        &       -9.134    &        4.513     \\\\\n",
       "\\textbf{X\\_8}           &       7.4781  &        2.258     &     3.312  &         0.001        &        3.052    &       11.904     \\\\\n",
       "\\textbf{X\\_9}           &       3.8586  &        1.836     &     2.101  &         0.036        &        0.259    &        7.458     \\\\\n",
       "\\textbf{X\\_10}          &       3.2644  &        1.772     &     1.843  &         0.065        &       -0.208    &        6.737     \\\\\n",
       "\\textbf{X\\_11}          &       5.3296  &        3.152     &     1.691  &         0.091        &       -0.848    &       11.507     \\\\\n",
       "\\textbf{X\\_12}          &       0.7504  &        1.161     &     0.646  &         0.518        &       -1.525    &        3.026     \\\\\n",
       "\\textbf{X\\_13}          &       1.7368  &        0.936     &     1.855  &         0.064        &       -0.098    &        3.571     \\\\\n",
       "\\textbf{X\\_14}          &       1.8819  &        1.258     &     1.497  &         0.135        &       -0.583    &        4.347     \\\\\n",
       "\\textbf{X\\_15}          &       3.2434  &        2.083     &     1.557  &         0.120        &       -0.840    &        7.327     \\\\\n",
       "\\textbf{X\\_16}          &       3.8807  &        2.369     &     1.638  &         0.101        &       -0.763    &        8.524     \\\\\n",
       "\\textbf{X\\_17}          &       3.1145  &        2.067     &     1.507  &         0.132        &       -0.937    &        7.166     \\\\\n",
       "\\textbf{X\\_18}          &       2.1760  &        1.050     &     2.072  &         0.038        &        0.118    &        4.234     \\\\\n",
       "\\textbf{X\\_19}          &       1.1250  &        1.279     &     0.880  &         0.379        &       -1.381    &        3.631     \\\\\n",
       "\\textbf{X\\_20}          &       2.5944  &        1.731     &     1.499  &         0.134        &       -0.798    &        5.987     \\\\\n",
       "\\textbf{X\\_21}          &       4.4446  &        1.968     &     2.258  &         0.024        &        0.587    &        8.302     \\\\\n",
       "\\textbf{X\\_22}          &       3.4897  &        1.357     &     2.572  &         0.010        &        0.831    &        6.149     \\\\\n",
       "\\textbf{X\\_23}          &       3.5801  &        1.542     &     2.321  &         0.020        &        0.557    &        6.603     \\\\\n",
       "\\textbf{X\\_24}          &       3.1724  &        1.428     &     2.221  &         0.026        &        0.373    &        5.972     \\\\\n",
       "\\textbf{X\\_25}          &       2.8846  &        1.214     &     2.375  &         0.018        &        0.504    &        5.265     \\\\\n",
       "\\textbf{X\\_26}          &       1.4874  &        0.860     &     1.729  &         0.084        &       -0.199    &        3.174     \\\\\n",
       "\\textbf{X\\_27}          &       3.2482  &        1.753     &     1.853  &         0.064        &       -0.187    &        6.684     \\\\\n",
       "\\textbf{X\\_28}          &       3.8579  &        1.620     &     2.381  &         0.017        &        0.683    &        7.033     \\\\\n",
       "\\textbf{X\\_29}          &       4.3395  &        1.113     &     3.900  &         0.000        &        2.159    &        6.520     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 3424540.174 & \\textbf{  Durbin-Watson:     } &        1.982     \\\\\n",
       "\\textbf{Prob(Omnibus):} &     0.000   & \\textbf{  Jarque-Bera (JB):  } & 10925565598.826  \\\\\n",
       "\\textbf{Skew:}          &    15.080   & \\textbf{  Prob(JB):          } &         0.00     \\\\\n",
       "\\textbf{Kurtosis:}      &   386.049   & \\textbf{  Cond. No.          } &         19.4     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors are robust to cluster correlation (cluster)"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:               mobility   R-squared:                       0.021\n",
       "Model:                            OLS   Adj. R-squared:                  0.021\n",
       "Method:                 Least Squares   F-statistic:                       nan\n",
       "Date:                Wed, 11 Jun 2025   Prob (F-statistic):                nan\n",
       "Time:                        14:34:20   Log-Likelihood:            -1.0633e+07\n",
       "No. Observations:             1776075   AIC:                         2.127e+07\n",
       "Df Residuals:                 1776022   BIC:                         2.127e+07\n",
       "Df Model:                          52                                         \n",
       "Covariance Type:              cluster                                         \n",
       "==================================================================================\n",
       "                     coef    std err          z      P>|z|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------\n",
       "C(strike)[67]     29.2688      0.682     42.930      0.000      27.933      30.605\n",
       "C(strike)[68]     27.4778      0.677     40.612      0.000      26.152      28.804\n",
       "C(strike)[69]     32.8900      0.703     46.786      0.000      31.512      34.268\n",
       "C(strike)[70]     23.4764      0.723     32.481      0.000      22.060      24.893\n",
       "C(strike)[71]     31.9136      0.703     45.388      0.000      30.535      33.292\n",
       "C(strike)[72]     33.9907      0.710     47.897      0.000      32.600      35.382\n",
       "C(strike)[75]     39.5380      0.712     55.552      0.000      38.143      40.933\n",
       "C(strike)[76]     61.8580      0.714     86.657      0.000      60.459      63.257\n",
       "C(strike)[77]     30.7782      0.712     43.212      0.000      29.382      32.174\n",
       "C(strike)[78]     26.9157      0.716     37.604      0.000      25.513      28.319\n",
       "C(strike)[81]     38.9212      0.700     55.632      0.000      37.550      40.292\n",
       "C(strike)[82]     26.8207      0.706     38.000      0.000      25.437      28.204\n",
       "C(strike)[83]     27.2563      0.704     38.738      0.000      25.877      28.635\n",
       "C(strike)[85]     25.9158      0.701     36.958      0.000      24.541      27.290\n",
       "C(strike)[87]     19.6960      0.711     27.705      0.000      18.303      21.089\n",
       "C(strike)[90]     14.4516      0.706     20.470      0.000      13.068      15.835\n",
       "C(strike)[92]     15.2271      0.710     21.440      0.000      13.835      16.619\n",
       "C(strike)[95]     18.8967      0.726     26.021      0.000      17.473      20.320\n",
       "C(strike)[96]     39.9156      0.710     56.217      0.000      38.524      41.307\n",
       "C(strike)[97]     13.6752      0.713     19.192      0.000      12.279      15.072\n",
       "C(strike)[100]    28.9422      0.707     40.936      0.000      27.556      30.328\n",
       "C(strike)[101]    43.7898      0.706     62.024      0.000      42.406      45.174\n",
       "C(strike)[102]    33.8561      0.709     47.731      0.000      32.466      35.246\n",
       "C(strike)[106]    23.4448      0.654     35.843      0.000      22.163      24.727\n",
       "C(strike)[107]    31.7199      0.652     48.671      0.000      30.443      32.997\n",
       "X_1               -2.8660      3.223     -0.889      0.374      -9.184       3.452\n",
       "X_2               -3.0150      3.509     -0.859      0.390      -9.893       3.863\n",
       "X_3               -1.8165      3.342     -0.543      0.587      -8.367       4.734\n",
       "X_4               -0.1910      2.007     -0.095      0.924      -4.125       3.743\n",
       "X_5                0.5259      0.895      0.588      0.557      -1.228       2.280\n",
       "X_7               -2.3105      3.481     -0.664      0.507      -9.134       4.513\n",
       "X_8                7.4781      2.258      3.312      0.001       3.052      11.904\n",
       "X_9                3.8586      1.836      2.101      0.036       0.259       7.458\n",
       "X_10               3.2644      1.772      1.843      0.065      -0.208       6.737\n",
       "X_11               5.3296      3.152      1.691      0.091      -0.848      11.507\n",
       "X_12               0.7504      1.161      0.646      0.518      -1.525       3.026\n",
       "X_13               1.7368      0.936      1.855      0.064      -0.098       3.571\n",
       "X_14               1.8819      1.258      1.497      0.135      -0.583       4.347\n",
       "X_15               3.2434      2.083      1.557      0.120      -0.840       7.327\n",
       "X_16               3.8807      2.369      1.638      0.101      -0.763       8.524\n",
       "X_17               3.1145      2.067      1.507      0.132      -0.937       7.166\n",
       "X_18               2.1760      1.050      2.072      0.038       0.118       4.234\n",
       "X_19               1.1250      1.279      0.880      0.379      -1.381       3.631\n",
       "X_20               2.5944      1.731      1.499      0.134      -0.798       5.987\n",
       "X_21               4.4446      1.968      2.258      0.024       0.587       8.302\n",
       "X_22               3.4897      1.357      2.572      0.010       0.831       6.149\n",
       "X_23               3.5801      1.542      2.321      0.020       0.557       6.603\n",
       "X_24               3.1724      1.428      2.221      0.026       0.373       5.972\n",
       "X_25               2.8846      1.214      2.375      0.018       0.504       5.265\n",
       "X_26               1.4874      0.860      1.729      0.084      -0.199       3.174\n",
       "X_27               3.2482      1.753      1.853      0.064      -0.187       6.684\n",
       "X_28               3.8579      1.620      2.381      0.017       0.683       7.033\n",
       "X_29               4.3395      1.113      3.900      0.000       2.159       6.520\n",
       "==============================================================================\n",
       "Omnibus:                  3424540.174   Durbin-Watson:                   1.982\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):      10925565598.826\n",
       "Skew:                          15.080   Prob(JB):                         0.00\n",
       "Kurtosis:                     386.049   Cond. No.                         19.4\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors are robust to cluster correlation (cluster)\n",
       "\"\"\""
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# run the event study regression for mobility around strikes\n",
    "# 7 lags and 21 leads\n",
    "# time vars provide the parameters of interest\n",
    "# drop the day = -2 variable for difference in means and to avoid multicollinearity\n",
    "# use strike fixed effects and cluster SE by strike\n",
    "\n",
    "reg = sm.ols('mobility ~ ' + var_form + ' +  C(strike) - 1', \n",
    "             data=df_period3).fit(cov_type='cluster',\n",
    "                          cov_kwds={'groups': df_period3['strike'].values})\n",
    "\n",
    "# save parameters and confidence intervals for plotting\n",
    "conf_int = reg.conf_int()\n",
    "res_period3 = pd.DataFrame({\n",
    "    'params': reg.params,\n",
    "    'ci_l': conf_int[0],  # lower bound\n",
    "    'ci_h': conf_int[1],  # upper bound\n",
    "})\n",
    "\n",
    "# print regression summary\n",
    "reg.summary()\n",
    "# reindex the results to go from day = -7 to day = 21"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   parsed_date  strike_quantile\n",
      "0      1/12/10                0\n",
      "1      1/20/10                0\n",
      "2      1/31/10                0\n",
      "3      3/14/10                0\n",
      "4      3/15/10                0\n",
      "5      5/25/10                0\n",
      "6       5/5/11                0\n",
      "7       6/3/11                0\n",
      "8      6/10/11                0\n",
      "9      6/18/11                0\n",
      "10     7/14/11                0\n",
      "11      8/1/11                0\n",
      "12     8/24/11                0\n",
      "13     8/25/11                0\n",
      "14      9/1/11                0\n",
      "15      9/5/11                0\n",
      "16     9/21/11                0\n",
      "17     10/5/11                0\n",
      "18    10/14/11                0\n",
      "19    10/14/11                0\n",
      "20    12/23/11                0\n",
      "21     1/30/12                0\n",
      "22      3/9/12                0\n",
      "23     3/10/12                0\n",
      "24     3/11/12                0\n",
      "25     3/13/12                1\n",
      "26     3/18/12                1\n",
      "27     3/18/12                1\n",
      "28     3/22/12                1\n",
      "29     3/30/12                1\n",
      "30      4/1/12                1\n",
      "31      4/7/12                1\n",
      "32      4/8/12                1\n",
      "33     4/11/12                1\n",
      "34     4/14/12                1\n",
      "35     4/16/12                1\n",
      "36     4/18/12                1\n",
      "37     4/21/12                1\n",
      "38     4/22/12                1\n",
      "39     4/26/12                1\n",
      "40     4/30/12                1\n",
      "41     4/30/12                1\n",
      "42      5/2/12                1\n",
      "43      5/6/12                1\n",
      "44     5/10/12                1\n",
      "45     5/10/12                1\n",
      "46     5/12/12                1\n",
      "47     5/12/12                1\n",
      "48     5/14/12                1\n",
      "49     5/14/12                2\n",
      "50     5/15/12                2\n",
      "51     5/16/12                2\n",
      "52     5/17/12                2\n",
      "53     5/17/12                2\n",
      "54     5/19/12                2\n",
      "55     5/28/12                2\n",
      "56     5/28/12                2\n",
      "57      6/1/12                2\n",
      "58      6/7/12                2\n",
      "59     6/11/12                2\n",
      "60     6/13/12                2\n",
      "61     6/13/12                2\n",
      "62     6/15/12                2\n",
      "63     6/19/12                2\n",
      "64     6/25/12                2\n",
      "65      7/3/12                2\n",
      "66      8/4/12                2\n",
      "67      8/6/12                2\n",
      "68      8/7/12                2\n",
      "69     8/31/12                2\n",
      "70      9/2/12                2\n",
      "71      9/5/12                2\n",
      "72    10/18/12                2\n",
      "73    10/21/12                2\n"
     ]
    }
   ],
   "source": [
    "# show the entire parsed date and strike quantile columns from the strikes dataframe\n",
    "print(strikes[['parsed_date', 'strike_quantile']].to_string())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "event_res_period1 = res_period1[res_period1.index.to_series().str.startswith(\"X_\")].copy()\n",
    "event_res_period1 = event_res_period1.reset_index().rename(columns={'index': 'varname'})\n",
    "event_res_period1['day'] = event_res_period1['varname'].str.extract(r'X_(\\d+)').astype(float).astype('Int64') - 8\n",
    "\n",
    "event_res_period2 = res_period2[res_period2.index.to_series().str.startswith(\"X_\")].copy()\n",
    "event_res_period2 = event_res_period2.reset_index().rename(columns={'index': 'varname'})\n",
    "event_res_period2['day'] = event_res_period2['varname'].str.extract(r'X_(\\d+)').astype(float).astype('Int64') - 8\n",
    "\n",
    "event_res_period3 = res_period3[res_period3.index.to_series().str.startswith(\"X_\")].copy()\n",
    "event_res_period3 = event_res_period3.reset_index().rename(columns={'index': 'varname'})\n",
    "event_res_period3['day'] = event_res_period3['varname'].str.extract(r'X_(\\d+)').astype(float).astype('Int64') - 8\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_three_group(event_res_period1, event_res_period2, event_res_period3,\n",
    "                       label1='Jan. 2010 -- Mar. 2012', label2='Mar. 2012 -- May 2012', label3='May 2012 -- Oct. 2012',\n",
    "                       color1='C0', color2='C1', color3='C2',\n",
    "                       ylabel='Distance (km)',\n",
    "                       outfile='figures/figureX_epoch.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Prep 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load distance data\n",
    "distance = pd.read_csv('data_mobility/distance_daily.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>strike</th>\n",
       "      <th>day</th>\n",
       "      <th>id</th>\n",
       "      <th>distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>-7</td>\n",
       "      <td>15700</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-7</td>\n",
       "      <td>362258</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>-7</td>\n",
       "      <td>546570</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>-7</td>\n",
       "      <td>578001</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>-7</td>\n",
       "      <td>581213</td>\n",
       "      <td>52.588072</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   strike  day      id   distance\n",
       "0       1   -7   15700   0.000000\n",
       "1       1   -7  362258   0.000000\n",
       "2       1   -7  546570   0.000000\n",
       "3       1   -7  578001   0.000000\n",
       "4       1   -7  581213  52.588072"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the distance dataframe records the distance of proximal individuals to the strike region\n",
    "# each day for 7 days before the strike, on the day of the strike, and 56 days after\n",
    "# the strike column records the strike ID, the day column records the number of days to the strike,\n",
    "# the id column records a unique anonymized ID for each individual, and\n",
    "# the distance column records the distance from the strike region in miles\n",
    "distance.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>strike</th>\n",
       "      <th>day</th>\n",
       "      <th>id</th>\n",
       "      <th>distance</th>\n",
       "      <th>new_id</th>\n",
       "      <th>parsed_date</th>\n",
       "      <th>district</th>\n",
       "      <th>governorate</th>\n",
       "      <th>district_id</th>\n",
       "      <th>latitude</th>\n",
       "      <th>...</th>\n",
       "      <th>total_killed_high</th>\n",
       "      <th>total_killed_low</th>\n",
       "      <th>rank_militants</th>\n",
       "      <th>hour</th>\n",
       "      <th>hour_from_am</th>\n",
       "      <th>time_of_day</th>\n",
       "      <th>high_ranking</th>\n",
       "      <th>density</th>\n",
       "      <th>high_density</th>\n",
       "      <th>strike_quantile</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>-7</td>\n",
       "      <td>15700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1/12/10</td>\n",
       "      <td>Merkhah As Sufla</td>\n",
       "      <td>Shabwah</td>\n",
       "      <td>2109</td>\n",
       "      <td>14.646474</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>day</td>\n",
       "      <td>0</td>\n",
       "      <td>12.915622</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-7</td>\n",
       "      <td>362258</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1/12/10</td>\n",
       "      <td>Merkhah As Sufla</td>\n",
       "      <td>Shabwah</td>\n",
       "      <td>2109</td>\n",
       "      <td>14.646474</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>day</td>\n",
       "      <td>0</td>\n",
       "      <td>12.915622</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>-7</td>\n",
       "      <td>546570</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1/12/10</td>\n",
       "      <td>Merkhah As Sufla</td>\n",
       "      <td>Shabwah</td>\n",
       "      <td>2109</td>\n",
       "      <td>14.646474</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>day</td>\n",
       "      <td>0</td>\n",
       "      <td>12.915622</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>-7</td>\n",
       "      <td>578001</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1/12/10</td>\n",
       "      <td>Merkhah As Sufla</td>\n",
       "      <td>Shabwah</td>\n",
       "      <td>2109</td>\n",
       "      <td>14.646474</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>day</td>\n",
       "      <td>0</td>\n",
       "      <td>12.915622</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>-7</td>\n",
       "      <td>581213</td>\n",
       "      <td>52.588072</td>\n",
       "      <td>1</td>\n",
       "      <td>1/12/10</td>\n",
       "      <td>Merkhah As Sufla</td>\n",
       "      <td>Shabwah</td>\n",
       "      <td>2109</td>\n",
       "      <td>14.646474</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>day</td>\n",
       "      <td>0</td>\n",
       "      <td>12.915622</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>-7</td>\n",
       "      <td>738484</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1/12/10</td>\n",
       "      <td>Merkhah As Sufla</td>\n",
       "      <td>Shabwah</td>\n",
       "      <td>2109</td>\n",
       "      <td>14.646474</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>day</td>\n",
       "      <td>0</td>\n",
       "      <td>12.915622</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>-7</td>\n",
       "      <td>1267410</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1/12/10</td>\n",
       "      <td>Merkhah As Sufla</td>\n",
       "      <td>Shabwah</td>\n",
       "      <td>2109</td>\n",
       "      <td>14.646474</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>day</td>\n",
       "      <td>0</td>\n",
       "      <td>12.915622</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>-7</td>\n",
       "      <td>1586539</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1/12/10</td>\n",
       "      <td>Merkhah As Sufla</td>\n",
       "      <td>Shabwah</td>\n",
       "      <td>2109</td>\n",
       "      <td>14.646474</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>day</td>\n",
       "      <td>0</td>\n",
       "      <td>12.915622</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>-7</td>\n",
       "      <td>1598769</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1/12/10</td>\n",
       "      <td>Merkhah As Sufla</td>\n",
       "      <td>Shabwah</td>\n",
       "      <td>2109</td>\n",
       "      <td>14.646474</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>day</td>\n",
       "      <td>0</td>\n",
       "      <td>12.915622</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>-7</td>\n",
       "      <td>1609801</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1/12/10</td>\n",
       "      <td>Merkhah As Sufla</td>\n",
       "      <td>Shabwah</td>\n",
       "      <td>2109</td>\n",
       "      <td>14.646474</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>day</td>\n",
       "      <td>0</td>\n",
       "      <td>12.915622</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>-7</td>\n",
       "      <td>1952504</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1/12/10</td>\n",
       "      <td>Merkhah As Sufla</td>\n",
       "      <td>Shabwah</td>\n",
       "      <td>2109</td>\n",
       "      <td>14.646474</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>day</td>\n",
       "      <td>0</td>\n",
       "      <td>12.915622</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>-7</td>\n",
       "      <td>2339388</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1/12/10</td>\n",
       "      <td>Merkhah As Sufla</td>\n",
       "      <td>Shabwah</td>\n",
       "      <td>2109</td>\n",
       "      <td>14.646474</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>day</td>\n",
       "      <td>0</td>\n",
       "      <td>12.915622</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>-7</td>\n",
       "      <td>2360625</td>\n",
       "      <td>33.123486</td>\n",
       "      <td>1</td>\n",
       "      <td>1/12/10</td>\n",
       "      <td>Merkhah As Sufla</td>\n",
       "      <td>Shabwah</td>\n",
       "      <td>2109</td>\n",
       "      <td>14.646474</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>day</td>\n",
       "      <td>0</td>\n",
       "      <td>12.915622</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>-7</td>\n",
       "      <td>2432738</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1/12/10</td>\n",
       "      <td>Merkhah As Sufla</td>\n",
       "      <td>Shabwah</td>\n",
       "      <td>2109</td>\n",
       "      <td>14.646474</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>day</td>\n",
       "      <td>0</td>\n",
       "      <td>12.915622</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>-7</td>\n",
       "      <td>2488685</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1/12/10</td>\n",
       "      <td>Merkhah As Sufla</td>\n",
       "      <td>Shabwah</td>\n",
       "      <td>2109</td>\n",
       "      <td>14.646474</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>day</td>\n",
       "      <td>0</td>\n",
       "      <td>12.915622</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>-7</td>\n",
       "      <td>2628021</td>\n",
       "      <td>25.975646</td>\n",
       "      <td>1</td>\n",
       "      <td>1/12/10</td>\n",
       "      <td>Merkhah As Sufla</td>\n",
       "      <td>Shabwah</td>\n",
       "      <td>2109</td>\n",
       "      <td>14.646474</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>day</td>\n",
       "      <td>0</td>\n",
       "      <td>12.915622</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "      <td>-7</td>\n",
       "      <td>2841826</td>\n",
       "      <td>30.190810</td>\n",
       "      <td>1</td>\n",
       "      <td>1/12/10</td>\n",
       "      <td>Merkhah As Sufla</td>\n",
       "      <td>Shabwah</td>\n",
       "      <td>2109</td>\n",
       "      <td>14.646474</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>day</td>\n",
       "      <td>0</td>\n",
       "      <td>12.915622</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>-7</td>\n",
       "      <td>3032283</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1/12/10</td>\n",
       "      <td>Merkhah As Sufla</td>\n",
       "      <td>Shabwah</td>\n",
       "      <td>2109</td>\n",
       "      <td>14.646474</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>day</td>\n",
       "      <td>0</td>\n",
       "      <td>12.915622</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>-7</td>\n",
       "      <td>4160885</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1/12/10</td>\n",
       "      <td>Merkhah As Sufla</td>\n",
       "      <td>Shabwah</td>\n",
       "      <td>2109</td>\n",
       "      <td>14.646474</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>day</td>\n",
       "      <td>0</td>\n",
       "      <td>12.915622</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>-7</td>\n",
       "      <td>4906263</td>\n",
       "      <td>1.561266</td>\n",
       "      <td>1</td>\n",
       "      <td>1/12/10</td>\n",
       "      <td>Merkhah As Sufla</td>\n",
       "      <td>Shabwah</td>\n",
       "      <td>2109</td>\n",
       "      <td>14.646474</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>day</td>\n",
       "      <td>0</td>\n",
       "      <td>12.915622</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    strike  day       id   distance  new_id parsed_date          district  \\\n",
       "0        1   -7    15700   0.000000       1     1/12/10  Merkhah As Sufla   \n",
       "1        1   -7   362258   0.000000       1     1/12/10  Merkhah As Sufla   \n",
       "2        1   -7   546570   0.000000       1     1/12/10  Merkhah As Sufla   \n",
       "3        1   -7   578001   0.000000       1     1/12/10  Merkhah As Sufla   \n",
       "4        1   -7   581213  52.588072       1     1/12/10  Merkhah As Sufla   \n",
       "5        1   -7   738484   0.000000       1     1/12/10  Merkhah As Sufla   \n",
       "6        1   -7  1267410   0.000000       1     1/12/10  Merkhah As Sufla   \n",
       "7        1   -7  1586539   0.000000       1     1/12/10  Merkhah As Sufla   \n",
       "8        1   -7  1598769   0.000000       1     1/12/10  Merkhah As Sufla   \n",
       "9        1   -7  1609801   0.000000       1     1/12/10  Merkhah As Sufla   \n",
       "10       1   -7  1952504   0.000000       1     1/12/10  Merkhah As Sufla   \n",
       "11       1   -7  2339388   0.000000       1     1/12/10  Merkhah As Sufla   \n",
       "12       1   -7  2360625  33.123486       1     1/12/10  Merkhah As Sufla   \n",
       "13       1   -7  2432738   0.000000       1     1/12/10  Merkhah As Sufla   \n",
       "14       1   -7  2488685   0.000000       1     1/12/10  Merkhah As Sufla   \n",
       "15       1   -7  2628021  25.975646       1     1/12/10  Merkhah As Sufla   \n",
       "16       1   -7  2841826  30.190810       1     1/12/10  Merkhah As Sufla   \n",
       "17       1   -7  3032283   0.000000       1     1/12/10  Merkhah As Sufla   \n",
       "18       1   -7  4160885   0.000000       1     1/12/10  Merkhah As Sufla   \n",
       "19       1   -7  4906263   1.561266       1     1/12/10  Merkhah As Sufla   \n",
       "\n",
       "   governorate  district_id   latitude  ...  total_killed_high  \\\n",
       "0      Shabwah         2109  14.646474  ...                  2   \n",
       "1      Shabwah         2109  14.646474  ...                  2   \n",
       "2      Shabwah         2109  14.646474  ...                  2   \n",
       "3      Shabwah         2109  14.646474  ...                  2   \n",
       "4      Shabwah         2109  14.646474  ...                  2   \n",
       "5      Shabwah         2109  14.646474  ...                  2   \n",
       "6      Shabwah         2109  14.646474  ...                  2   \n",
       "7      Shabwah         2109  14.646474  ...                  2   \n",
       "8      Shabwah         2109  14.646474  ...                  2   \n",
       "9      Shabwah         2109  14.646474  ...                  2   \n",
       "10     Shabwah         2109  14.646474  ...                  2   \n",
       "11     Shabwah         2109  14.646474  ...                  2   \n",
       "12     Shabwah         2109  14.646474  ...                  2   \n",
       "13     Shabwah         2109  14.646474  ...                  2   \n",
       "14     Shabwah         2109  14.646474  ...                  2   \n",
       "15     Shabwah         2109  14.646474  ...                  2   \n",
       "16     Shabwah         2109  14.646474  ...                  2   \n",
       "17     Shabwah         2109  14.646474  ...                  2   \n",
       "18     Shabwah         2109  14.646474  ...                  2   \n",
       "19     Shabwah         2109  14.646474  ...                  2   \n",
       "\n",
       "   total_killed_low rank_militants  hour  hour_from_am  time_of_day  \\\n",
       "0                 1            NaN    10             4          day   \n",
       "1                 1            NaN    10             4          day   \n",
       "2                 1            NaN    10             4          day   \n",
       "3                 1            NaN    10             4          day   \n",
       "4                 1            NaN    10             4          day   \n",
       "5                 1            NaN    10             4          day   \n",
       "6                 1            NaN    10             4          day   \n",
       "7                 1            NaN    10             4          day   \n",
       "8                 1            NaN    10             4          day   \n",
       "9                 1            NaN    10             4          day   \n",
       "10                1            NaN    10             4          day   \n",
       "11                1            NaN    10             4          day   \n",
       "12                1            NaN    10             4          day   \n",
       "13                1            NaN    10             4          day   \n",
       "14                1            NaN    10             4          day   \n",
       "15                1            NaN    10             4          day   \n",
       "16                1            NaN    10             4          day   \n",
       "17                1            NaN    10             4          day   \n",
       "18                1            NaN    10             4          day   \n",
       "19                1            NaN    10             4          day   \n",
       "\n",
       "    high_ranking    density  high_density strike_quantile  \n",
       "0              0  12.915622             0               0  \n",
       "1              0  12.915622             0               0  \n",
       "2              0  12.915622             0               0  \n",
       "3              0  12.915622             0               0  \n",
       "4              0  12.915622             0               0  \n",
       "5              0  12.915622             0               0  \n",
       "6              0  12.915622             0               0  \n",
       "7              0  12.915622             0               0  \n",
       "8              0  12.915622             0               0  \n",
       "9              0  12.915622             0               0  \n",
       "10             0  12.915622             0               0  \n",
       "11             0  12.915622             0               0  \n",
       "12             0  12.915622             0               0  \n",
       "13             0  12.915622             0               0  \n",
       "14             0  12.915622             0               0  \n",
       "15             0  12.915622             0               0  \n",
       "16             0  12.915622             0               0  \n",
       "17             0  12.915622             0               0  \n",
       "18             0  12.915622             0               0  \n",
       "19             0  12.915622             0               0  \n",
       "\n",
       "[20 rows x 27 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distance  = distance.merge(strikes, left_on = 'strike', right_on = \"new_id\", how = \"left\")\n",
    "distance.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prep distance data for event study regression\n",
    "\n",
    "# shift the day index to only include positive values for the regression\n",
    "shift = 8\n",
    "distance['day'] = distance['day'] + shift\n",
    "\n",
    "# add lead/lag indicator variables corresponding to the day index\n",
    "xs = pd.get_dummies(distance['day'], prefix='X')\n",
    "df = distance.merge(xs, left_index=True, right_index=True)\n",
    "\n",
    "# Ensure X columns are numeric, not boolean\n",
    "for col in df.columns:\n",
    "    if col.startswith(\"X_\"):\n",
    "        df[col] = df[col].astype(int)\n",
    "\n",
    "# convert mobility in miles to kilometers\n",
    "km_scalar = 1.60934    # scalar to convert miles to kilometers\n",
    "df['distance'] = df['distance'] * km_scalar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create string that includes all the lead/lag indicator variables\n",
    "# this string will be used in the statsmodel regression formula\n",
    "# drop the -2 day variable to avoid multicollinearity\n",
    "\n",
    "var = []\n",
    "for i in range(-7, 56+1):\n",
    "    if i == -2:\n",
    "        continue\n",
    "    var.append('X_' + str(i+shift))\n",
    "var_form = ' + '.join(var)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure S13B: Distance Results with Pop. Density Subgroups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# subset distance df by high and low density\n",
    "df_high = df[df['high_density'] == 1]\n",
    "df_low = df[df['high_density'] == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>distance</td>     <th>  R-squared:         </th>  <td>   0.022</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.022</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>     nan</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 21 May 2025</td> <th>  Prob (F-statistic):</th>   <td>   nan</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>12:45:45</td>     <th>  Log-Likelihood:    </th> <td>-1.4276e+07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>2570945</td>     <th>  AIC:               </th>  <td>2.855e+07</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>2570846</td>     <th>  BIC:               </th>  <td>2.855e+07</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    98</td>      <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>       <td>cluster</td>     <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>           <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[3]</th>   <td>   14.8618</td> <td>    1.098</td> <td>   13.537</td> <td> 0.000</td> <td>   12.710</td> <td>   17.014</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[4]</th>   <td>   10.2132</td> <td>    1.072</td> <td>    9.527</td> <td> 0.000</td> <td>    8.112</td> <td>   12.314</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[5]</th>   <td>   26.5344</td> <td>    1.113</td> <td>   23.842</td> <td> 0.000</td> <td>   24.353</td> <td>   28.716</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[6]</th>   <td>   26.4701</td> <td>    1.122</td> <td>   23.591</td> <td> 0.000</td> <td>   24.271</td> <td>   28.669</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[9]</th>   <td>   10.9003</td> <td>    1.018</td> <td>   10.709</td> <td> 0.000</td> <td>    8.905</td> <td>   12.895</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[10]</th>  <td>   17.9188</td> <td>    0.980</td> <td>   18.291</td> <td> 0.000</td> <td>   15.999</td> <td>   19.839</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[15]</th>  <td>   13.3486</td> <td>    0.560</td> <td>   23.839</td> <td> 0.000</td> <td>   12.251</td> <td>   14.446</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[17]</th>  <td>   22.8656</td> <td>    1.007</td> <td>   22.716</td> <td> 0.000</td> <td>   20.893</td> <td>   24.838</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[19]</th>  <td>   24.7524</td> <td>    0.887</td> <td>   27.905</td> <td> 0.000</td> <td>   23.014</td> <td>   26.491</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[31]</th>  <td>   13.1287</td> <td>    1.170</td> <td>   11.223</td> <td> 0.000</td> <td>   10.836</td> <td>   15.421</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[32]</th>  <td>   21.2767</td> <td>    1.117</td> <td>   19.055</td> <td> 0.000</td> <td>   19.088</td> <td>   23.465</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[33]</th>  <td>   15.7025</td> <td>    1.094</td> <td>   14.351</td> <td> 0.000</td> <td>   13.558</td> <td>   17.847</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[36]</th>  <td>   11.0650</td> <td>    1.096</td> <td>   10.094</td> <td> 0.000</td> <td>    8.917</td> <td>   13.213</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[39]</th>  <td>   10.2619</td> <td>    1.081</td> <td>    9.496</td> <td> 0.000</td> <td>    8.144</td> <td>   12.380</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[41]</th>  <td>   11.5392</td> <td>    1.097</td> <td>   10.520</td> <td> 0.000</td> <td>    9.389</td> <td>   13.689</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[45]</th>  <td>   98.0990</td> <td>    0.983</td> <td>   99.754</td> <td> 0.000</td> <td>   96.172</td> <td>  100.026</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[47]</th>  <td>   15.6711</td> <td>    1.093</td> <td>   14.338</td> <td> 0.000</td> <td>   13.529</td> <td>   17.813</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[49]</th>  <td>   18.5671</td> <td>    1.099</td> <td>   16.902</td> <td> 0.000</td> <td>   16.414</td> <td>   20.720</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[50]</th>  <td>   16.2610</td> <td>    1.118</td> <td>   14.547</td> <td> 0.000</td> <td>   14.070</td> <td>   18.452</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[54]</th>  <td>   22.6660</td> <td>    1.118</td> <td>   20.267</td> <td> 0.000</td> <td>   20.474</td> <td>   24.858</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[56]</th>  <td>   29.6899</td> <td>    1.018</td> <td>   29.159</td> <td> 0.000</td> <td>   27.694</td> <td>   31.685</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[57]</th>  <td>   17.4989</td> <td>    1.111</td> <td>   15.753</td> <td> 0.000</td> <td>   15.322</td> <td>   19.676</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[61]</th>  <td>   26.2371</td> <td>    1.067</td> <td>   24.590</td> <td> 0.000</td> <td>   24.146</td> <td>   28.328</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[64]</th>  <td>   23.0863</td> <td>    1.084</td> <td>   21.289</td> <td> 0.000</td> <td>   20.961</td> <td>   25.212</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[69]</th>  <td>   22.3628</td> <td>    0.986</td> <td>   22.692</td> <td> 0.000</td> <td>   20.431</td> <td>   24.294</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[70]</th>  <td>   40.8091</td> <td>    1.072</td> <td>   38.073</td> <td> 0.000</td> <td>   38.708</td> <td>   42.910</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[72]</th>  <td>   16.7137</td> <td>    1.117</td> <td>   14.958</td> <td> 0.000</td> <td>   14.524</td> <td>   18.904</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[75]</th>  <td>    7.0441</td> <td>    1.082</td> <td>    6.509</td> <td> 0.000</td> <td>    4.923</td> <td>    9.165</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[76]</th>  <td>   34.6156</td> <td>    1.040</td> <td>   33.269</td> <td> 0.000</td> <td>   32.576</td> <td>   36.655</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[77]</th>  <td>   27.6322</td> <td>    1.114</td> <td>   24.810</td> <td> 0.000</td> <td>   25.449</td> <td>   29.815</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[78]</th>  <td>   23.5185</td> <td>    1.073</td> <td>   21.920</td> <td> 0.000</td> <td>   21.416</td> <td>   25.621</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[90]</th>  <td>    7.0200</td> <td>    1.188</td> <td>    5.909</td> <td> 0.000</td> <td>    4.691</td> <td>    9.349</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[96]</th>  <td>    5.9185</td> <td>    1.104</td> <td>    5.363</td> <td> 0.000</td> <td>    3.756</td> <td>    8.082</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[97]</th>  <td>   30.2873</td> <td>    1.142</td> <td>   26.518</td> <td> 0.000</td> <td>   28.049</td> <td>   32.526</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[101]</th> <td>    7.1169</td> <td>    1.128</td> <td>    6.309</td> <td> 0.000</td> <td>    4.906</td> <td>    9.328</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[102]</th> <td>   26.9922</td> <td>    1.137</td> <td>   23.743</td> <td> 0.000</td> <td>   24.764</td> <td>   29.220</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_1</th>            <td>   -1.3647</td> <td>    1.140</td> <td>   -1.197</td> <td> 0.231</td> <td>   -3.599</td> <td>    0.870</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_2</th>            <td>   -0.2414</td> <td>    0.772</td> <td>   -0.313</td> <td> 0.755</td> <td>   -1.755</td> <td>    1.272</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_3</th>            <td>   -1.0186</td> <td>    0.976</td> <td>   -1.044</td> <td> 0.296</td> <td>   -2.931</td> <td>    0.894</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_4</th>            <td>   -1.0411</td> <td>    0.920</td> <td>   -1.132</td> <td> 0.258</td> <td>   -2.844</td> <td>    0.762</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_5</th>            <td>   -0.2805</td> <td>    0.443</td> <td>   -0.633</td> <td> 0.527</td> <td>   -1.150</td> <td>    0.589</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_7</th>            <td>    0.3796</td> <td>    0.403</td> <td>    0.941</td> <td> 0.347</td> <td>   -0.411</td> <td>    1.170</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_8</th>            <td>   -0.1006</td> <td>    0.466</td> <td>   -0.216</td> <td> 0.829</td> <td>   -1.014</td> <td>    0.812</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_9</th>            <td>    1.6880</td> <td>    0.481</td> <td>    3.512</td> <td> 0.000</td> <td>    0.746</td> <td>    2.630</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_10</th>           <td>    2.2351</td> <td>    0.467</td> <td>    4.789</td> <td> 0.000</td> <td>    1.320</td> <td>    3.150</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_11</th>           <td>    3.3379</td> <td>    0.578</td> <td>    5.775</td> <td> 0.000</td> <td>    2.205</td> <td>    4.471</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_12</th>           <td>    3.2493</td> <td>    0.532</td> <td>    6.109</td> <td> 0.000</td> <td>    2.207</td> <td>    4.292</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_13</th>           <td>    3.7940</td> <td>    0.693</td> <td>    5.476</td> <td> 0.000</td> <td>    2.436</td> <td>    5.152</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_14</th>           <td>    4.5579</td> <td>    0.627</td> <td>    7.265</td> <td> 0.000</td> <td>    3.328</td> <td>    5.788</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_15</th>           <td>    4.4084</td> <td>    0.765</td> <td>    5.765</td> <td> 0.000</td> <td>    2.910</td> <td>    5.907</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_16</th>           <td>    4.8935</td> <td>    0.649</td> <td>    7.539</td> <td> 0.000</td> <td>    3.621</td> <td>    6.166</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_17</th>           <td>    6.0696</td> <td>    1.034</td> <td>    5.871</td> <td> 0.000</td> <td>    4.043</td> <td>    8.096</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_18</th>           <td>    5.7670</td> <td>    0.912</td> <td>    6.327</td> <td> 0.000</td> <td>    3.980</td> <td>    7.554</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_19</th>           <td>    5.5364</td> <td>    0.865</td> <td>    6.401</td> <td> 0.000</td> <td>    3.841</td> <td>    7.232</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_20</th>           <td>    6.0158</td> <td>    1.007</td> <td>    5.976</td> <td> 0.000</td> <td>    4.043</td> <td>    7.989</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_21</th>           <td>    6.9565</td> <td>    1.008</td> <td>    6.905</td> <td> 0.000</td> <td>    4.982</td> <td>    8.931</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_22</th>           <td>    7.3436</td> <td>    0.892</td> <td>    8.233</td> <td> 0.000</td> <td>    5.595</td> <td>    9.092</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_23</th>           <td>    7.2469</td> <td>    0.915</td> <td>    7.921</td> <td> 0.000</td> <td>    5.454</td> <td>    9.040</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_24</th>           <td>    8.2728</td> <td>    1.264</td> <td>    6.544</td> <td> 0.000</td> <td>    5.795</td> <td>   10.750</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_25</th>           <td>    7.7666</td> <td>    1.046</td> <td>    7.426</td> <td> 0.000</td> <td>    5.717</td> <td>    9.817</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_26</th>           <td>    8.3531</td> <td>    1.298</td> <td>    6.438</td> <td> 0.000</td> <td>    5.810</td> <td>   10.896</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_27</th>           <td>    8.3968</td> <td>    1.302</td> <td>    6.450</td> <td> 0.000</td> <td>    5.845</td> <td>   10.948</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_28</th>           <td>    8.8407</td> <td>    1.158</td> <td>    7.633</td> <td> 0.000</td> <td>    6.571</td> <td>   11.111</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_29</th>           <td>    9.8520</td> <td>    1.186</td> <td>    8.310</td> <td> 0.000</td> <td>    7.528</td> <td>   12.176</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_30</th>           <td>    9.6463</td> <td>    1.221</td> <td>    7.897</td> <td> 0.000</td> <td>    7.252</td> <td>   12.040</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_31</th>           <td>    9.0338</td> <td>    1.163</td> <td>    7.768</td> <td> 0.000</td> <td>    6.754</td> <td>   11.313</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_32</th>           <td>    8.9288</td> <td>    1.357</td> <td>    6.579</td> <td> 0.000</td> <td>    6.269</td> <td>   11.589</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_33</th>           <td>    8.7276</td> <td>    1.236</td> <td>    7.059</td> <td> 0.000</td> <td>    6.304</td> <td>   11.151</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_34</th>           <td>   10.2884</td> <td>    1.641</td> <td>    6.270</td> <td> 0.000</td> <td>    7.072</td> <td>   13.504</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_35</th>           <td>   10.0088</td> <td>    1.390</td> <td>    7.201</td> <td> 0.000</td> <td>    7.284</td> <td>   12.733</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_36</th>           <td>    9.8609</td> <td>    1.324</td> <td>    7.447</td> <td> 0.000</td> <td>    7.266</td> <td>   12.456</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_37</th>           <td>    9.9335</td> <td>    1.351</td> <td>    7.355</td> <td> 0.000</td> <td>    7.286</td> <td>   12.581</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_38</th>           <td>   10.0783</td> <td>    1.594</td> <td>    6.323</td> <td> 0.000</td> <td>    6.954</td> <td>   13.202</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_39</th>           <td>   10.6881</td> <td>    1.708</td> <td>    6.257</td> <td> 0.000</td> <td>    7.340</td> <td>   14.036</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_40</th>           <td>    9.7917</td> <td>    1.857</td> <td>    5.273</td> <td> 0.000</td> <td>    6.152</td> <td>   13.431</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_41</th>           <td>    9.8676</td> <td>    1.716</td> <td>    5.750</td> <td> 0.000</td> <td>    6.504</td> <td>   13.231</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_42</th>           <td>   10.5276</td> <td>    1.765</td> <td>    5.963</td> <td> 0.000</td> <td>    7.067</td> <td>   13.988</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_43</th>           <td>   10.8059</td> <td>    1.665</td> <td>    6.491</td> <td> 0.000</td> <td>    7.543</td> <td>   14.069</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_44</th>           <td>   11.0790</td> <td>    1.629</td> <td>    6.803</td> <td> 0.000</td> <td>    7.887</td> <td>   14.271</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_45</th>           <td>   11.4275</td> <td>    1.825</td> <td>    6.261</td> <td> 0.000</td> <td>    7.850</td> <td>   15.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_46</th>           <td>   11.0596</td> <td>    1.733</td> <td>    6.381</td> <td> 0.000</td> <td>    7.663</td> <td>   14.457</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_47</th>           <td>   11.7465</td> <td>    1.977</td> <td>    5.941</td> <td> 0.000</td> <td>    7.871</td> <td>   15.622</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_48</th>           <td>   11.2575</td> <td>    1.922</td> <td>    5.858</td> <td> 0.000</td> <td>    7.491</td> <td>   15.024</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_49</th>           <td>   11.7963</td> <td>    1.832</td> <td>    6.440</td> <td> 0.000</td> <td>    8.206</td> <td>   15.386</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_50</th>           <td>   11.6895</td> <td>    1.781</td> <td>    6.563</td> <td> 0.000</td> <td>    8.198</td> <td>   15.180</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_51</th>           <td>   12.1778</td> <td>    1.806</td> <td>    6.744</td> <td> 0.000</td> <td>    8.639</td> <td>   15.717</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_52</th>           <td>   11.9481</td> <td>    2.012</td> <td>    5.938</td> <td> 0.000</td> <td>    8.004</td> <td>   15.892</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_53</th>           <td>   11.5860</td> <td>    1.987</td> <td>    5.831</td> <td> 0.000</td> <td>    7.692</td> <td>   15.480</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_54</th>           <td>   11.2383</td> <td>    2.200</td> <td>    5.108</td> <td> 0.000</td> <td>    6.926</td> <td>   15.551</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_55</th>           <td>   11.7748</td> <td>    2.010</td> <td>    5.858</td> <td> 0.000</td> <td>    7.836</td> <td>   15.714</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_56</th>           <td>   11.6563</td> <td>    1.936</td> <td>    6.020</td> <td> 0.000</td> <td>    7.861</td> <td>   15.451</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_57</th>           <td>   11.4587</td> <td>    1.900</td> <td>    6.032</td> <td> 0.000</td> <td>    7.736</td> <td>   15.182</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_58</th>           <td>   11.5300</td> <td>    2.036</td> <td>    5.663</td> <td> 0.000</td> <td>    7.539</td> <td>   15.521</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_59</th>           <td>   11.5298</td> <td>    2.138</td> <td>    5.392</td> <td> 0.000</td> <td>    7.339</td> <td>   15.721</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_60</th>           <td>   11.4295</td> <td>    2.423</td> <td>    4.717</td> <td> 0.000</td> <td>    6.680</td> <td>   16.179</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_61</th>           <td>    9.8850</td> <td>    2.373</td> <td>    4.165</td> <td> 0.000</td> <td>    5.234</td> <td>   14.536</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_62</th>           <td>   11.3043</td> <td>    2.436</td> <td>    4.640</td> <td> 0.000</td> <td>    6.529</td> <td>   16.079</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_63</th>           <td>   11.0689</td> <td>    2.315</td> <td>    4.781</td> <td> 0.000</td> <td>    6.531</td> <td>   15.607</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_64</th>           <td>   10.4536</td> <td>    2.010</td> <td>    5.201</td> <td> 0.000</td> <td>    6.514</td> <td>   14.393</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>2803756.032</td> <th>  Durbin-Watson:     </th>   <td>   1.977</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>   <td> 0.000</td>    <th>  Jarque-Bera (JB):  </th> <td>223433199.247</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>            <td> 5.662</td>    <th>  Prob(JB):          </th>   <td>    0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>        <td>47.244</td>    <th>  Cond. No.          </th>   <td>    32.8</td>   \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors are robust to cluster correlation (cluster)"
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &     distance     & \\textbf{  R-squared:         } &       0.022    \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &       0.022    \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &         nan    \\\\\n",
       "\\textbf{Date:}             & Wed, 21 May 2025 & \\textbf{  Prob (F-statistic):} &        nan     \\\\\n",
       "\\textbf{Time:}             &     12:45:45     & \\textbf{  Log-Likelihood:    } &  -1.4276e+07   \\\\\n",
       "\\textbf{No. Observations:} &     2570945      & \\textbf{  AIC:               } &   2.855e+07    \\\\\n",
       "\\textbf{Df Residuals:}     &     2570846      & \\textbf{  BIC:               } &   2.855e+07    \\\\\n",
       "\\textbf{Df Model:}         &          98      & \\textbf{                     } &                \\\\\n",
       "\\textbf{Covariance Type:}  &     cluster      & \\textbf{                     } &                \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                        & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{C(strike)[3]}   &      14.8618  &        1.098     &    13.537  &         0.000        &       12.710    &       17.014     \\\\\n",
       "\\textbf{C(strike)[4]}   &      10.2132  &        1.072     &     9.527  &         0.000        &        8.112    &       12.314     \\\\\n",
       "\\textbf{C(strike)[5]}   &      26.5344  &        1.113     &    23.842  &         0.000        &       24.353    &       28.716     \\\\\n",
       "\\textbf{C(strike)[6]}   &      26.4701  &        1.122     &    23.591  &         0.000        &       24.271    &       28.669     \\\\\n",
       "\\textbf{C(strike)[9]}   &      10.9003  &        1.018     &    10.709  &         0.000        &        8.905    &       12.895     \\\\\n",
       "\\textbf{C(strike)[10]}  &      17.9188  &        0.980     &    18.291  &         0.000        &       15.999    &       19.839     \\\\\n",
       "\\textbf{C(strike)[15]}  &      13.3486  &        0.560     &    23.839  &         0.000        &       12.251    &       14.446     \\\\\n",
       "\\textbf{C(strike)[17]}  &      22.8656  &        1.007     &    22.716  &         0.000        &       20.893    &       24.838     \\\\\n",
       "\\textbf{C(strike)[19]}  &      24.7524  &        0.887     &    27.905  &         0.000        &       23.014    &       26.491     \\\\\n",
       "\\textbf{C(strike)[31]}  &      13.1287  &        1.170     &    11.223  &         0.000        &       10.836    &       15.421     \\\\\n",
       "\\textbf{C(strike)[32]}  &      21.2767  &        1.117     &    19.055  &         0.000        &       19.088    &       23.465     \\\\\n",
       "\\textbf{C(strike)[33]}  &      15.7025  &        1.094     &    14.351  &         0.000        &       13.558    &       17.847     \\\\\n",
       "\\textbf{C(strike)[36]}  &      11.0650  &        1.096     &    10.094  &         0.000        &        8.917    &       13.213     \\\\\n",
       "\\textbf{C(strike)[39]}  &      10.2619  &        1.081     &     9.496  &         0.000        &        8.144    &       12.380     \\\\\n",
       "\\textbf{C(strike)[41]}  &      11.5392  &        1.097     &    10.520  &         0.000        &        9.389    &       13.689     \\\\\n",
       "\\textbf{C(strike)[45]}  &      98.0990  &        0.983     &    99.754  &         0.000        &       96.172    &      100.026     \\\\\n",
       "\\textbf{C(strike)[47]}  &      15.6711  &        1.093     &    14.338  &         0.000        &       13.529    &       17.813     \\\\\n",
       "\\textbf{C(strike)[49]}  &      18.5671  &        1.099     &    16.902  &         0.000        &       16.414    &       20.720     \\\\\n",
       "\\textbf{C(strike)[50]}  &      16.2610  &        1.118     &    14.547  &         0.000        &       14.070    &       18.452     \\\\\n",
       "\\textbf{C(strike)[54]}  &      22.6660  &        1.118     &    20.267  &         0.000        &       20.474    &       24.858     \\\\\n",
       "\\textbf{C(strike)[56]}  &      29.6899  &        1.018     &    29.159  &         0.000        &       27.694    &       31.685     \\\\\n",
       "\\textbf{C(strike)[57]}  &      17.4989  &        1.111     &    15.753  &         0.000        &       15.322    &       19.676     \\\\\n",
       "\\textbf{C(strike)[61]}  &      26.2371  &        1.067     &    24.590  &         0.000        &       24.146    &       28.328     \\\\\n",
       "\\textbf{C(strike)[64]}  &      23.0863  &        1.084     &    21.289  &         0.000        &       20.961    &       25.212     \\\\\n",
       "\\textbf{C(strike)[69]}  &      22.3628  &        0.986     &    22.692  &         0.000        &       20.431    &       24.294     \\\\\n",
       "\\textbf{C(strike)[70]}  &      40.8091  &        1.072     &    38.073  &         0.000        &       38.708    &       42.910     \\\\\n",
       "\\textbf{C(strike)[72]}  &      16.7137  &        1.117     &    14.958  &         0.000        &       14.524    &       18.904     \\\\\n",
       "\\textbf{C(strike)[75]}  &       7.0441  &        1.082     &     6.509  &         0.000        &        4.923    &        9.165     \\\\\n",
       "\\textbf{C(strike)[76]}  &      34.6156  &        1.040     &    33.269  &         0.000        &       32.576    &       36.655     \\\\\n",
       "\\textbf{C(strike)[77]}  &      27.6322  &        1.114     &    24.810  &         0.000        &       25.449    &       29.815     \\\\\n",
       "\\textbf{C(strike)[78]}  &      23.5185  &        1.073     &    21.920  &         0.000        &       21.416    &       25.621     \\\\\n",
       "\\textbf{C(strike)[90]}  &       7.0200  &        1.188     &     5.909  &         0.000        &        4.691    &        9.349     \\\\\n",
       "\\textbf{C(strike)[96]}  &       5.9185  &        1.104     &     5.363  &         0.000        &        3.756    &        8.082     \\\\\n",
       "\\textbf{C(strike)[97]}  &      30.2873  &        1.142     &    26.518  &         0.000        &       28.049    &       32.526     \\\\\n",
       "\\textbf{C(strike)[101]} &       7.1169  &        1.128     &     6.309  &         0.000        &        4.906    &        9.328     \\\\\n",
       "\\textbf{C(strike)[102]} &      26.9922  &        1.137     &    23.743  &         0.000        &       24.764    &       29.220     \\\\\n",
       "\\textbf{X\\_1}           &      -1.3647  &        1.140     &    -1.197  &         0.231        &       -3.599    &        0.870     \\\\\n",
       "\\textbf{X\\_2}           &      -0.2414  &        0.772     &    -0.313  &         0.755        &       -1.755    &        1.272     \\\\\n",
       "\\textbf{X\\_3}           &      -1.0186  &        0.976     &    -1.044  &         0.296        &       -2.931    &        0.894     \\\\\n",
       "\\textbf{X\\_4}           &      -1.0411  &        0.920     &    -1.132  &         0.258        &       -2.844    &        0.762     \\\\\n",
       "\\textbf{X\\_5}           &      -0.2805  &        0.443     &    -0.633  &         0.527        &       -1.150    &        0.589     \\\\\n",
       "\\textbf{X\\_7}           &       0.3796  &        0.403     &     0.941  &         0.347        &       -0.411    &        1.170     \\\\\n",
       "\\textbf{X\\_8}           &      -0.1006  &        0.466     &    -0.216  &         0.829        &       -1.014    &        0.812     \\\\\n",
       "\\textbf{X\\_9}           &       1.6880  &        0.481     &     3.512  &         0.000        &        0.746    &        2.630     \\\\\n",
       "\\textbf{X\\_10}          &       2.2351  &        0.467     &     4.789  &         0.000        &        1.320    &        3.150     \\\\\n",
       "\\textbf{X\\_11}          &       3.3379  &        0.578     &     5.775  &         0.000        &        2.205    &        4.471     \\\\\n",
       "\\textbf{X\\_12}          &       3.2493  &        0.532     &     6.109  &         0.000        &        2.207    &        4.292     \\\\\n",
       "\\textbf{X\\_13}          &       3.7940  &        0.693     &     5.476  &         0.000        &        2.436    &        5.152     \\\\\n",
       "\\textbf{X\\_14}          &       4.5579  &        0.627     &     7.265  &         0.000        &        3.328    &        5.788     \\\\\n",
       "\\textbf{X\\_15}          &       4.4084  &        0.765     &     5.765  &         0.000        &        2.910    &        5.907     \\\\\n",
       "\\textbf{X\\_16}          &       4.8935  &        0.649     &     7.539  &         0.000        &        3.621    &        6.166     \\\\\n",
       "\\textbf{X\\_17}          &       6.0696  &        1.034     &     5.871  &         0.000        &        4.043    &        8.096     \\\\\n",
       "\\textbf{X\\_18}          &       5.7670  &        0.912     &     6.327  &         0.000        &        3.980    &        7.554     \\\\\n",
       "\\textbf{X\\_19}          &       5.5364  &        0.865     &     6.401  &         0.000        &        3.841    &        7.232     \\\\\n",
       "\\textbf{X\\_20}          &       6.0158  &        1.007     &     5.976  &         0.000        &        4.043    &        7.989     \\\\\n",
       "\\textbf{X\\_21}          &       6.9565  &        1.008     &     6.905  &         0.000        &        4.982    &        8.931     \\\\\n",
       "\\textbf{X\\_22}          &       7.3436  &        0.892     &     8.233  &         0.000        &        5.595    &        9.092     \\\\\n",
       "\\textbf{X\\_23}          &       7.2469  &        0.915     &     7.921  &         0.000        &        5.454    &        9.040     \\\\\n",
       "\\textbf{X\\_24}          &       8.2728  &        1.264     &     6.544  &         0.000        &        5.795    &       10.750     \\\\\n",
       "\\textbf{X\\_25}          &       7.7666  &        1.046     &     7.426  &         0.000        &        5.717    &        9.817     \\\\\n",
       "\\textbf{X\\_26}          &       8.3531  &        1.298     &     6.438  &         0.000        &        5.810    &       10.896     \\\\\n",
       "\\textbf{X\\_27}          &       8.3968  &        1.302     &     6.450  &         0.000        &        5.845    &       10.948     \\\\\n",
       "\\textbf{X\\_28}          &       8.8407  &        1.158     &     7.633  &         0.000        &        6.571    &       11.111     \\\\\n",
       "\\textbf{X\\_29}          &       9.8520  &        1.186     &     8.310  &         0.000        &        7.528    &       12.176     \\\\\n",
       "\\textbf{X\\_30}          &       9.6463  &        1.221     &     7.897  &         0.000        &        7.252    &       12.040     \\\\\n",
       "\\textbf{X\\_31}          &       9.0338  &        1.163     &     7.768  &         0.000        &        6.754    &       11.313     \\\\\n",
       "\\textbf{X\\_32}          &       8.9288  &        1.357     &     6.579  &         0.000        &        6.269    &       11.589     \\\\\n",
       "\\textbf{X\\_33}          &       8.7276  &        1.236     &     7.059  &         0.000        &        6.304    &       11.151     \\\\\n",
       "\\textbf{X\\_34}          &      10.2884  &        1.641     &     6.270  &         0.000        &        7.072    &       13.504     \\\\\n",
       "\\textbf{X\\_35}          &      10.0088  &        1.390     &     7.201  &         0.000        &        7.284    &       12.733     \\\\\n",
       "\\textbf{X\\_36}          &       9.8609  &        1.324     &     7.447  &         0.000        &        7.266    &       12.456     \\\\\n",
       "\\textbf{X\\_37}          &       9.9335  &        1.351     &     7.355  &         0.000        &        7.286    &       12.581     \\\\\n",
       "\\textbf{X\\_38}          &      10.0783  &        1.594     &     6.323  &         0.000        &        6.954    &       13.202     \\\\\n",
       "\\textbf{X\\_39}          &      10.6881  &        1.708     &     6.257  &         0.000        &        7.340    &       14.036     \\\\\n",
       "\\textbf{X\\_40}          &       9.7917  &        1.857     &     5.273  &         0.000        &        6.152    &       13.431     \\\\\n",
       "\\textbf{X\\_41}          &       9.8676  &        1.716     &     5.750  &         0.000        &        6.504    &       13.231     \\\\\n",
       "\\textbf{X\\_42}          &      10.5276  &        1.765     &     5.963  &         0.000        &        7.067    &       13.988     \\\\\n",
       "\\textbf{X\\_43}          &      10.8059  &        1.665     &     6.491  &         0.000        &        7.543    &       14.069     \\\\\n",
       "\\textbf{X\\_44}          &      11.0790  &        1.629     &     6.803  &         0.000        &        7.887    &       14.271     \\\\\n",
       "\\textbf{X\\_45}          &      11.4275  &        1.825     &     6.261  &         0.000        &        7.850    &       15.005     \\\\\n",
       "\\textbf{X\\_46}          &      11.0596  &        1.733     &     6.381  &         0.000        &        7.663    &       14.457     \\\\\n",
       "\\textbf{X\\_47}          &      11.7465  &        1.977     &     5.941  &         0.000        &        7.871    &       15.622     \\\\\n",
       "\\textbf{X\\_48}          &      11.2575  &        1.922     &     5.858  &         0.000        &        7.491    &       15.024     \\\\\n",
       "\\textbf{X\\_49}          &      11.7963  &        1.832     &     6.440  &         0.000        &        8.206    &       15.386     \\\\\n",
       "\\textbf{X\\_50}          &      11.6895  &        1.781     &     6.563  &         0.000        &        8.198    &       15.180     \\\\\n",
       "\\textbf{X\\_51}          &      12.1778  &        1.806     &     6.744  &         0.000        &        8.639    &       15.717     \\\\\n",
       "\\textbf{X\\_52}          &      11.9481  &        2.012     &     5.938  &         0.000        &        8.004    &       15.892     \\\\\n",
       "\\textbf{X\\_53}          &      11.5860  &        1.987     &     5.831  &         0.000        &        7.692    &       15.480     \\\\\n",
       "\\textbf{X\\_54}          &      11.2383  &        2.200     &     5.108  &         0.000        &        6.926    &       15.551     \\\\\n",
       "\\textbf{X\\_55}          &      11.7748  &        2.010     &     5.858  &         0.000        &        7.836    &       15.714     \\\\\n",
       "\\textbf{X\\_56}          &      11.6563  &        1.936     &     6.020  &         0.000        &        7.861    &       15.451     \\\\\n",
       "\\textbf{X\\_57}          &      11.4587  &        1.900     &     6.032  &         0.000        &        7.736    &       15.182     \\\\\n",
       "\\textbf{X\\_58}          &      11.5300  &        2.036     &     5.663  &         0.000        &        7.539    &       15.521     \\\\\n",
       "\\textbf{X\\_59}          &      11.5298  &        2.138     &     5.392  &         0.000        &        7.339    &       15.721     \\\\\n",
       "\\textbf{X\\_60}          &      11.4295  &        2.423     &     4.717  &         0.000        &        6.680    &       16.179     \\\\\n",
       "\\textbf{X\\_61}          &       9.8850  &        2.373     &     4.165  &         0.000        &        5.234    &       14.536     \\\\\n",
       "\\textbf{X\\_62}          &      11.3043  &        2.436     &     4.640  &         0.000        &        6.529    &       16.079     \\\\\n",
       "\\textbf{X\\_63}          &      11.0689  &        2.315     &     4.781  &         0.000        &        6.531    &       15.607     \\\\\n",
       "\\textbf{X\\_64}          &      10.4536  &        2.010     &     5.201  &         0.000        &        6.514    &       14.393     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 2803756.032 & \\textbf{  Durbin-Watson:     } &       1.977    \\\\\n",
       "\\textbf{Prob(Omnibus):} &     0.000   & \\textbf{  Jarque-Bera (JB):  } & 223433199.247  \\\\\n",
       "\\textbf{Skew:}          &     5.662   & \\textbf{  Prob(JB):          } &        0.00    \\\\\n",
       "\\textbf{Kurtosis:}      &    47.244   & \\textbf{  Cond. No.          } &        32.8    \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors are robust to cluster correlation (cluster)"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:               distance   R-squared:                       0.022\n",
       "Model:                            OLS   Adj. R-squared:                  0.022\n",
       "Method:                 Least Squares   F-statistic:                       nan\n",
       "Date:                Wed, 21 May 2025   Prob (F-statistic):                nan\n",
       "Time:                        12:45:45   Log-Likelihood:            -1.4276e+07\n",
       "No. Observations:             2570945   AIC:                         2.855e+07\n",
       "Df Residuals:                 2570846   BIC:                         2.855e+07\n",
       "Df Model:                          98                                         \n",
       "Covariance Type:              cluster                                         \n",
       "==================================================================================\n",
       "                     coef    std err          z      P>|z|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------\n",
       "C(strike)[3]      14.8618      1.098     13.537      0.000      12.710      17.014\n",
       "C(strike)[4]      10.2132      1.072      9.527      0.000       8.112      12.314\n",
       "C(strike)[5]      26.5344      1.113     23.842      0.000      24.353      28.716\n",
       "C(strike)[6]      26.4701      1.122     23.591      0.000      24.271      28.669\n",
       "C(strike)[9]      10.9003      1.018     10.709      0.000       8.905      12.895\n",
       "C(strike)[10]     17.9188      0.980     18.291      0.000      15.999      19.839\n",
       "C(strike)[15]     13.3486      0.560     23.839      0.000      12.251      14.446\n",
       "C(strike)[17]     22.8656      1.007     22.716      0.000      20.893      24.838\n",
       "C(strike)[19]     24.7524      0.887     27.905      0.000      23.014      26.491\n",
       "C(strike)[31]     13.1287      1.170     11.223      0.000      10.836      15.421\n",
       "C(strike)[32]     21.2767      1.117     19.055      0.000      19.088      23.465\n",
       "C(strike)[33]     15.7025      1.094     14.351      0.000      13.558      17.847\n",
       "C(strike)[36]     11.0650      1.096     10.094      0.000       8.917      13.213\n",
       "C(strike)[39]     10.2619      1.081      9.496      0.000       8.144      12.380\n",
       "C(strike)[41]     11.5392      1.097     10.520      0.000       9.389      13.689\n",
       "C(strike)[45]     98.0990      0.983     99.754      0.000      96.172     100.026\n",
       "C(strike)[47]     15.6711      1.093     14.338      0.000      13.529      17.813\n",
       "C(strike)[49]     18.5671      1.099     16.902      0.000      16.414      20.720\n",
       "C(strike)[50]     16.2610      1.118     14.547      0.000      14.070      18.452\n",
       "C(strike)[54]     22.6660      1.118     20.267      0.000      20.474      24.858\n",
       "C(strike)[56]     29.6899      1.018     29.159      0.000      27.694      31.685\n",
       "C(strike)[57]     17.4989      1.111     15.753      0.000      15.322      19.676\n",
       "C(strike)[61]     26.2371      1.067     24.590      0.000      24.146      28.328\n",
       "C(strike)[64]     23.0863      1.084     21.289      0.000      20.961      25.212\n",
       "C(strike)[69]     22.3628      0.986     22.692      0.000      20.431      24.294\n",
       "C(strike)[70]     40.8091      1.072     38.073      0.000      38.708      42.910\n",
       "C(strike)[72]     16.7137      1.117     14.958      0.000      14.524      18.904\n",
       "C(strike)[75]      7.0441      1.082      6.509      0.000       4.923       9.165\n",
       "C(strike)[76]     34.6156      1.040     33.269      0.000      32.576      36.655\n",
       "C(strike)[77]     27.6322      1.114     24.810      0.000      25.449      29.815\n",
       "C(strike)[78]     23.5185      1.073     21.920      0.000      21.416      25.621\n",
       "C(strike)[90]      7.0200      1.188      5.909      0.000       4.691       9.349\n",
       "C(strike)[96]      5.9185      1.104      5.363      0.000       3.756       8.082\n",
       "C(strike)[97]     30.2873      1.142     26.518      0.000      28.049      32.526\n",
       "C(strike)[101]     7.1169      1.128      6.309      0.000       4.906       9.328\n",
       "C(strike)[102]    26.9922      1.137     23.743      0.000      24.764      29.220\n",
       "X_1               -1.3647      1.140     -1.197      0.231      -3.599       0.870\n",
       "X_2               -0.2414      0.772     -0.313      0.755      -1.755       1.272\n",
       "X_3               -1.0186      0.976     -1.044      0.296      -2.931       0.894\n",
       "X_4               -1.0411      0.920     -1.132      0.258      -2.844       0.762\n",
       "X_5               -0.2805      0.443     -0.633      0.527      -1.150       0.589\n",
       "X_7                0.3796      0.403      0.941      0.347      -0.411       1.170\n",
       "X_8               -0.1006      0.466     -0.216      0.829      -1.014       0.812\n",
       "X_9                1.6880      0.481      3.512      0.000       0.746       2.630\n",
       "X_10               2.2351      0.467      4.789      0.000       1.320       3.150\n",
       "X_11               3.3379      0.578      5.775      0.000       2.205       4.471\n",
       "X_12               3.2493      0.532      6.109      0.000       2.207       4.292\n",
       "X_13               3.7940      0.693      5.476      0.000       2.436       5.152\n",
       "X_14               4.5579      0.627      7.265      0.000       3.328       5.788\n",
       "X_15               4.4084      0.765      5.765      0.000       2.910       5.907\n",
       "X_16               4.8935      0.649      7.539      0.000       3.621       6.166\n",
       "X_17               6.0696      1.034      5.871      0.000       4.043       8.096\n",
       "X_18               5.7670      0.912      6.327      0.000       3.980       7.554\n",
       "X_19               5.5364      0.865      6.401      0.000       3.841       7.232\n",
       "X_20               6.0158      1.007      5.976      0.000       4.043       7.989\n",
       "X_21               6.9565      1.008      6.905      0.000       4.982       8.931\n",
       "X_22               7.3436      0.892      8.233      0.000       5.595       9.092\n",
       "X_23               7.2469      0.915      7.921      0.000       5.454       9.040\n",
       "X_24               8.2728      1.264      6.544      0.000       5.795      10.750\n",
       "X_25               7.7666      1.046      7.426      0.000       5.717       9.817\n",
       "X_26               8.3531      1.298      6.438      0.000       5.810      10.896\n",
       "X_27               8.3968      1.302      6.450      0.000       5.845      10.948\n",
       "X_28               8.8407      1.158      7.633      0.000       6.571      11.111\n",
       "X_29               9.8520      1.186      8.310      0.000       7.528      12.176\n",
       "X_30               9.6463      1.221      7.897      0.000       7.252      12.040\n",
       "X_31               9.0338      1.163      7.768      0.000       6.754      11.313\n",
       "X_32               8.9288      1.357      6.579      0.000       6.269      11.589\n",
       "X_33               8.7276      1.236      7.059      0.000       6.304      11.151\n",
       "X_34              10.2884      1.641      6.270      0.000       7.072      13.504\n",
       "X_35              10.0088      1.390      7.201      0.000       7.284      12.733\n",
       "X_36               9.8609      1.324      7.447      0.000       7.266      12.456\n",
       "X_37               9.9335      1.351      7.355      0.000       7.286      12.581\n",
       "X_38              10.0783      1.594      6.323      0.000       6.954      13.202\n",
       "X_39              10.6881      1.708      6.257      0.000       7.340      14.036\n",
       "X_40               9.7917      1.857      5.273      0.000       6.152      13.431\n",
       "X_41               9.8676      1.716      5.750      0.000       6.504      13.231\n",
       "X_42              10.5276      1.765      5.963      0.000       7.067      13.988\n",
       "X_43              10.8059      1.665      6.491      0.000       7.543      14.069\n",
       "X_44              11.0790      1.629      6.803      0.000       7.887      14.271\n",
       "X_45              11.4275      1.825      6.261      0.000       7.850      15.005\n",
       "X_46              11.0596      1.733      6.381      0.000       7.663      14.457\n",
       "X_47              11.7465      1.977      5.941      0.000       7.871      15.622\n",
       "X_48              11.2575      1.922      5.858      0.000       7.491      15.024\n",
       "X_49              11.7963      1.832      6.440      0.000       8.206      15.386\n",
       "X_50              11.6895      1.781      6.563      0.000       8.198      15.180\n",
       "X_51              12.1778      1.806      6.744      0.000       8.639      15.717\n",
       "X_52              11.9481      2.012      5.938      0.000       8.004      15.892\n",
       "X_53              11.5860      1.987      5.831      0.000       7.692      15.480\n",
       "X_54              11.2383      2.200      5.108      0.000       6.926      15.551\n",
       "X_55              11.7748      2.010      5.858      0.000       7.836      15.714\n",
       "X_56              11.6563      1.936      6.020      0.000       7.861      15.451\n",
       "X_57              11.4587      1.900      6.032      0.000       7.736      15.182\n",
       "X_58              11.5300      2.036      5.663      0.000       7.539      15.521\n",
       "X_59              11.5298      2.138      5.392      0.000       7.339      15.721\n",
       "X_60              11.4295      2.423      4.717      0.000       6.680      16.179\n",
       "X_61               9.8850      2.373      4.165      0.000       5.234      14.536\n",
       "X_62              11.3043      2.436      4.640      0.000       6.529      16.079\n",
       "X_63              11.0689      2.315      4.781      0.000       6.531      15.607\n",
       "X_64              10.4536      2.010      5.201      0.000       6.514      14.393\n",
       "==============================================================================\n",
       "Omnibus:                  2803756.032   Durbin-Watson:                   1.977\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):        223433199.247\n",
       "Skew:                           5.662   Prob(JB):                         0.00\n",
       "Kurtosis:                      47.244   Cond. No.                         32.8\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors are robust to cluster correlation (cluster)\n",
       "\"\"\""
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# run the event study regression for distance around strikes\n",
    "# 7 lags and 56 leads\n",
    "# time vars provide the parameters of interest\n",
    "# drop the day = -2 variable for difference in means and to avoid multicollinearity\n",
    "# use strike fixed effects and cluster SE by strike\n",
    "reg = sm.ols('distance ~ ' + var_form + ' + C(strike) - 1', \n",
    "             data=df_high).fit(cov_type='cluster',\n",
    "                          cov_kwds={'groups': np.array(df_high[['strike']])})\n",
    "\n",
    "\n",
    "# save parameters and confidence intervals for plotting\n",
    "conf_int = reg.conf_int()\n",
    "res_highdensity = pd.DataFrame({\n",
    "    'params': reg.params,\n",
    "    'ci_l': conf_int[0],  # lower bound\n",
    "    'ci_h': conf_int[1],  # upper bound\n",
    "})\n",
    "\n",
    "# print regression summary\n",
    "reg.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>distance</td>     <th>  R-squared:         </th>  <td>   0.047</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.046</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>     nan</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 21 May 2025</td> <th>  Prob (F-statistic):</th>   <td>   nan</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>12:45:56</td>     <th>  Log-Likelihood:    </th> <td>-2.1675e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>378132</td>      <th>  AIC:               </th>  <td>4.335e+06</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>378031</td>      <th>  BIC:               </th>  <td>4.336e+06</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>   100</td>      <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>       <td>cluster</td>     <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>           <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[1]</th>   <td>    8.6841</td> <td>    2.399</td> <td>    3.620</td> <td> 0.000</td> <td>    3.983</td> <td>   13.386</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[7]</th>   <td>   34.9706</td> <td>    2.345</td> <td>   14.913</td> <td> 0.000</td> <td>   30.375</td> <td>   39.567</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[8]</th>   <td>   -2.2891</td> <td>    2.441</td> <td>   -0.938</td> <td> 0.348</td> <td>   -7.074</td> <td>    2.495</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[11]</th>  <td>    6.4909</td> <td>    2.201</td> <td>    2.949</td> <td> 0.003</td> <td>    2.178</td> <td>   10.804</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[12]</th>  <td>    4.3378</td> <td>    2.644</td> <td>    1.641</td> <td> 0.101</td> <td>   -0.844</td> <td>    9.520</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[14]</th>  <td>   13.6676</td> <td>    2.352</td> <td>    5.811</td> <td> 0.000</td> <td>    9.058</td> <td>   18.277</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[20]</th>  <td>   20.1515</td> <td>    2.184</td> <td>    9.226</td> <td> 0.000</td> <td>   15.870</td> <td>   24.433</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[24]</th>  <td>   53.5148</td> <td>    2.366</td> <td>   22.614</td> <td> 0.000</td> <td>   48.877</td> <td>   58.153</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[26]</th>  <td>   22.5152</td> <td>    2.395</td> <td>    9.402</td> <td> 0.000</td> <td>   17.822</td> <td>   27.209</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[27]</th>  <td>   23.9252</td> <td>    2.317</td> <td>   10.325</td> <td> 0.000</td> <td>   19.383</td> <td>   28.467</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[28]</th>  <td>   15.0769</td> <td>    2.253</td> <td>    6.691</td> <td> 0.000</td> <td>   10.660</td> <td>   19.493</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[34]</th>  <td>    5.0173</td> <td>    2.409</td> <td>    2.082</td> <td> 0.037</td> <td>    0.295</td> <td>    9.740</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[35]</th>  <td>    4.1133</td> <td>    2.465</td> <td>    1.669</td> <td> 0.095</td> <td>   -0.717</td> <td>    8.944</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[37]</th>  <td>   -0.0476</td> <td>    2.468</td> <td>   -0.019</td> <td> 0.985</td> <td>   -4.885</td> <td>    4.790</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[38]</th>  <td>    1.5949</td> <td>    2.380</td> <td>    0.670</td> <td> 0.503</td> <td>   -3.071</td> <td>    6.260</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[40]</th>  <td>   21.4962</td> <td>    2.461</td> <td>    8.734</td> <td> 0.000</td> <td>   16.672</td> <td>   26.320</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[42]</th>  <td>   12.4249</td> <td>    2.421</td> <td>    5.133</td> <td> 0.000</td> <td>    7.680</td> <td>   17.169</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[43]</th>  <td>   14.5757</td> <td>    2.270</td> <td>    6.420</td> <td> 0.000</td> <td>   10.126</td> <td>   19.026</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[48]</th>  <td>   17.1657</td> <td>    2.397</td> <td>    7.162</td> <td> 0.000</td> <td>   12.468</td> <td>   21.863</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[51]</th>  <td>   53.7898</td> <td>    2.365</td> <td>   22.741</td> <td> 0.000</td> <td>   49.154</td> <td>   58.426</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[58]</th>  <td>   18.1732</td> <td>    2.228</td> <td>    8.155</td> <td> 0.000</td> <td>   13.805</td> <td>   22.541</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[59]</th>  <td>   10.1090</td> <td>    2.491</td> <td>    4.059</td> <td> 0.000</td> <td>    5.228</td> <td>   14.990</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[60]</th>  <td>   16.4680</td> <td>    2.236</td> <td>    7.363</td> <td> 0.000</td> <td>   12.085</td> <td>   20.851</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[62]</th>  <td>   37.0450</td> <td>    2.378</td> <td>   15.576</td> <td> 0.000</td> <td>   32.384</td> <td>   41.706</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[65]</th>  <td>   53.1275</td> <td>    2.347</td> <td>   22.633</td> <td> 0.000</td> <td>   48.527</td> <td>   57.728</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[67]</th>  <td>   13.3357</td> <td>    2.196</td> <td>    6.072</td> <td> 0.000</td> <td>    9.031</td> <td>   17.640</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[68]</th>  <td>   16.8176</td> <td>    2.164</td> <td>    7.771</td> <td> 0.000</td> <td>   12.576</td> <td>   21.059</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[71]</th>  <td>   62.8984</td> <td>    2.318</td> <td>   27.137</td> <td> 0.000</td> <td>   58.356</td> <td>   67.441</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[81]</th>  <td>   27.4297</td> <td>    2.365</td> <td>   11.598</td> <td> 0.000</td> <td>   22.794</td> <td>   32.065</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[82]</th>  <td>   31.3934</td> <td>    2.248</td> <td>   13.963</td> <td> 0.000</td> <td>   26.987</td> <td>   35.800</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[83]</th>  <td>   20.4277</td> <td>    2.227</td> <td>    9.175</td> <td> 0.000</td> <td>   16.064</td> <td>   24.792</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[85]</th>  <td>   29.4599</td> <td>    2.248</td> <td>   13.104</td> <td> 0.000</td> <td>   25.053</td> <td>   33.866</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[87]</th>  <td>    3.7434</td> <td>    1.991</td> <td>    1.880</td> <td> 0.060</td> <td>   -0.159</td> <td>    7.645</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[92]</th>  <td>   13.7049</td> <td>    2.310</td> <td>    5.932</td> <td> 0.000</td> <td>    9.177</td> <td>   18.233</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[95]</th>  <td>   56.5506</td> <td>    1.932</td> <td>   29.277</td> <td> 0.000</td> <td>   52.765</td> <td>   60.336</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[100]</th> <td>   86.0565</td> <td>    2.341</td> <td>   36.767</td> <td> 0.000</td> <td>   81.469</td> <td>   90.644</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[106]</th> <td>    4.4099</td> <td>    1.616</td> <td>    2.729</td> <td> 0.006</td> <td>    1.243</td> <td>    7.577</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[107]</th> <td>   48.0058</td> <td>    1.555</td> <td>   30.867</td> <td> 0.000</td> <td>   44.958</td> <td>   51.054</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_1</th>            <td>    0.3074</td> <td>    1.007</td> <td>    0.305</td> <td> 0.760</td> <td>   -1.666</td> <td>    2.281</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_2</th>            <td>    1.1458</td> <td>    1.787</td> <td>    0.641</td> <td> 0.521</td> <td>   -2.357</td> <td>    4.649</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_3</th>            <td>   -0.2222</td> <td>    1.287</td> <td>   -0.173</td> <td> 0.863</td> <td>   -2.744</td> <td>    2.300</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_4</th>            <td>   -0.7810</td> <td>    1.331</td> <td>   -0.587</td> <td> 0.557</td> <td>   -3.389</td> <td>    1.827</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_5</th>            <td>   -0.6583</td> <td>    1.560</td> <td>   -0.422</td> <td> 0.673</td> <td>   -3.716</td> <td>    2.399</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_7</th>            <td>    0.6975</td> <td>    1.799</td> <td>    0.388</td> <td> 0.698</td> <td>   -2.829</td> <td>    4.224</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_8</th>            <td>   -0.6186</td> <td>    1.811</td> <td>   -0.342</td> <td> 0.733</td> <td>   -4.168</td> <td>    2.931</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_9</th>            <td>    5.2860</td> <td>    2.195</td> <td>    2.409</td> <td> 0.016</td> <td>    0.985</td> <td>    9.587</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_10</th>           <td>    6.4273</td> <td>    2.220</td> <td>    2.895</td> <td> 0.004</td> <td>    2.075</td> <td>   10.779</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_11</th>           <td>    8.1163</td> <td>    2.443</td> <td>    3.323</td> <td> 0.001</td> <td>    3.329</td> <td>   12.904</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_12</th>           <td>   10.5316</td> <td>    2.620</td> <td>    4.020</td> <td> 0.000</td> <td>    5.397</td> <td>   15.666</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_13</th>           <td>    9.5368</td> <td>    2.607</td> <td>    3.659</td> <td> 0.000</td> <td>    4.428</td> <td>   14.646</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_14</th>           <td>   12.8704</td> <td>    3.280</td> <td>    3.924</td> <td> 0.000</td> <td>    6.442</td> <td>   19.298</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_15</th>           <td>   11.8915</td> <td>    2.535</td> <td>    4.690</td> <td> 0.000</td> <td>    6.922</td> <td>   16.861</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_16</th>           <td>   12.1660</td> <td>    3.121</td> <td>    3.898</td> <td> 0.000</td> <td>    6.049</td> <td>   18.284</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_17</th>           <td>   14.6754</td> <td>    3.180</td> <td>    4.615</td> <td> 0.000</td> <td>    8.442</td> <td>   20.909</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_18</th>           <td>   13.9034</td> <td>    3.481</td> <td>    3.994</td> <td> 0.000</td> <td>    7.081</td> <td>   20.726</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_19</th>           <td>   11.0857</td> <td>    2.253</td> <td>    4.922</td> <td> 0.000</td> <td>    6.671</td> <td>   15.501</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_20</th>           <td>   12.9464</td> <td>    2.744</td> <td>    4.718</td> <td> 0.000</td> <td>    7.568</td> <td>   18.325</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_21</th>           <td>   13.3436</td> <td>    2.505</td> <td>    5.328</td> <td> 0.000</td> <td>    8.435</td> <td>   18.252</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_22</th>           <td>   15.8124</td> <td>    2.856</td> <td>    5.536</td> <td> 0.000</td> <td>   10.215</td> <td>   21.410</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_23</th>           <td>   16.1379</td> <td>    3.128</td> <td>    5.158</td> <td> 0.000</td> <td>   10.006</td> <td>   22.270</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_24</th>           <td>   16.9843</td> <td>    2.677</td> <td>    6.344</td> <td> 0.000</td> <td>   11.737</td> <td>   22.232</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_25</th>           <td>   18.1176</td> <td>    2.936</td> <td>    6.171</td> <td> 0.000</td> <td>   12.363</td> <td>   23.872</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_26</th>           <td>   16.6632</td> <td>    2.652</td> <td>    6.284</td> <td> 0.000</td> <td>   11.466</td> <td>   21.860</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_27</th>           <td>   17.9490</td> <td>    2.176</td> <td>    8.249</td> <td> 0.000</td> <td>   13.684</td> <td>   22.214</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_28</th>           <td>   18.5940</td> <td>    2.508</td> <td>    7.413</td> <td> 0.000</td> <td>   13.678</td> <td>   23.510</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_29</th>           <td>   20.2843</td> <td>    2.695</td> <td>    7.527</td> <td> 0.000</td> <td>   15.003</td> <td>   25.566</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_30</th>           <td>   16.9527</td> <td>    2.724</td> <td>    6.223</td> <td> 0.000</td> <td>   11.613</td> <td>   22.292</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_31</th>           <td>   18.7964</td> <td>    3.024</td> <td>    6.216</td> <td> 0.000</td> <td>   12.870</td> <td>   24.723</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_32</th>           <td>   19.1026</td> <td>    3.318</td> <td>    5.757</td> <td> 0.000</td> <td>   12.600</td> <td>   25.605</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_33</th>           <td>   20.8021</td> <td>    2.806</td> <td>    7.413</td> <td> 0.000</td> <td>   15.302</td> <td>   26.302</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_34</th>           <td>   17.7033</td> <td>    2.834</td> <td>    6.248</td> <td> 0.000</td> <td>   12.150</td> <td>   23.257</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_35</th>           <td>   20.8472</td> <td>    3.153</td> <td>    6.612</td> <td> 0.000</td> <td>   14.667</td> <td>   27.027</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_36</th>           <td>   20.3511</td> <td>    3.740</td> <td>    5.442</td> <td> 0.000</td> <td>   13.022</td> <td>   27.681</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_37</th>           <td>   26.8554</td> <td>    3.377</td> <td>    7.953</td> <td> 0.000</td> <td>   20.237</td> <td>   33.474</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_38</th>           <td>   20.9924</td> <td>    4.160</td> <td>    5.046</td> <td> 0.000</td> <td>   12.839</td> <td>   29.146</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_39</th>           <td>   21.5411</td> <td>    3.781</td> <td>    5.698</td> <td> 0.000</td> <td>   14.131</td> <td>   28.951</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_40</th>           <td>   24.9044</td> <td>    4.649</td> <td>    5.357</td> <td> 0.000</td> <td>   15.793</td> <td>   34.016</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_41</th>           <td>   23.3910</td> <td>    4.569</td> <td>    5.120</td> <td> 0.000</td> <td>   14.436</td> <td>   32.346</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_42</th>           <td>   25.8306</td> <td>    4.796</td> <td>    5.386</td> <td> 0.000</td> <td>   16.430</td> <td>   35.231</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_43</th>           <td>   24.4808</td> <td>    3.802</td> <td>    6.439</td> <td> 0.000</td> <td>   17.029</td> <td>   31.933</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_44</th>           <td>   20.3821</td> <td>    3.777</td> <td>    5.397</td> <td> 0.000</td> <td>   12.980</td> <td>   27.784</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_45</th>           <td>   28.1470</td> <td>    2.950</td> <td>    9.542</td> <td> 0.000</td> <td>   22.365</td> <td>   33.929</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_46</th>           <td>   24.4605</td> <td>    3.798</td> <td>    6.440</td> <td> 0.000</td> <td>   17.016</td> <td>   31.905</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_47</th>           <td>   24.1590</td> <td>    4.586</td> <td>    5.268</td> <td> 0.000</td> <td>   15.171</td> <td>   33.147</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_48</th>           <td>   23.4124</td> <td>    4.536</td> <td>    5.161</td> <td> 0.000</td> <td>   14.522</td> <td>   32.303</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_49</th>           <td>   24.3156</td> <td>    4.196</td> <td>    5.794</td> <td> 0.000</td> <td>   16.091</td> <td>   32.540</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_50</th>           <td>   22.4903</td> <td>    4.283</td> <td>    5.251</td> <td> 0.000</td> <td>   14.095</td> <td>   30.885</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_51</th>           <td>   21.5832</td> <td>    4.168</td> <td>    5.179</td> <td> 0.000</td> <td>   13.415</td> <td>   29.752</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_52</th>           <td>   23.1871</td> <td>    4.476</td> <td>    5.180</td> <td> 0.000</td> <td>   14.413</td> <td>   31.961</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_53</th>           <td>   23.8819</td> <td>    3.623</td> <td>    6.593</td> <td> 0.000</td> <td>   16.782</td> <td>   30.982</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_54</th>           <td>   23.5255</td> <td>    4.479</td> <td>    5.253</td> <td> 0.000</td> <td>   14.748</td> <td>   32.303</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_55</th>           <td>   22.1215</td> <td>    4.070</td> <td>    5.435</td> <td> 0.000</td> <td>   14.145</td> <td>   30.098</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_56</th>           <td>   25.3757</td> <td>    5.230</td> <td>    4.852</td> <td> 0.000</td> <td>   15.125</td> <td>   35.626</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_57</th>           <td>   21.6280</td> <td>    4.395</td> <td>    4.921</td> <td> 0.000</td> <td>   13.014</td> <td>   30.242</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_58</th>           <td>   23.1101</td> <td>    3.893</td> <td>    5.936</td> <td> 0.000</td> <td>   15.479</td> <td>   30.741</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_59</th>           <td>   20.9405</td> <td>    3.566</td> <td>    5.872</td> <td> 0.000</td> <td>   13.951</td> <td>   27.930</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_60</th>           <td>   21.7947</td> <td>    4.031</td> <td>    5.407</td> <td> 0.000</td> <td>   13.895</td> <td>   29.694</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_61</th>           <td>   20.9569</td> <td>    3.933</td> <td>    5.329</td> <td> 0.000</td> <td>   13.249</td> <td>   28.665</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_62</th>           <td>   22.8382</td> <td>    3.659</td> <td>    6.242</td> <td> 0.000</td> <td>   15.667</td> <td>   30.010</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_63</th>           <td>   23.1336</td> <td>    3.025</td> <td>    7.647</td> <td> 0.000</td> <td>   17.205</td> <td>   29.063</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_64</th>           <td>   28.2942</td> <td>    3.446</td> <td>    8.210</td> <td> 0.000</td> <td>   21.540</td> <td>   35.049</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>353610.898</td> <th>  Durbin-Watson:     </th>   <td>   1.975</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>   <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>15889168.047</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>            <td> 4.560</td>   <th>  Prob(JB):          </th>   <td>    0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>        <td>33.419</td>   <th>  Cond. No.          </th>   <td>    34.5</td>  \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors are robust to cluster correlation (cluster)"
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &     distance     & \\textbf{  R-squared:         } &      0.047    \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &      0.046    \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &        nan    \\\\\n",
       "\\textbf{Date:}             & Wed, 21 May 2025 & \\textbf{  Prob (F-statistic):} &       nan     \\\\\n",
       "\\textbf{Time:}             &     12:45:56     & \\textbf{  Log-Likelihood:    } & -2.1675e+06   \\\\\n",
       "\\textbf{No. Observations:} &      378132      & \\textbf{  AIC:               } &  4.335e+06    \\\\\n",
       "\\textbf{Df Residuals:}     &      378031      & \\textbf{  BIC:               } &  4.336e+06    \\\\\n",
       "\\textbf{Df Model:}         &         100      & \\textbf{                     } &               \\\\\n",
       "\\textbf{Covariance Type:}  &     cluster      & \\textbf{                     } &               \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                        & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{C(strike)[1]}   &       8.6841  &        2.399     &     3.620  &         0.000        &        3.983    &       13.386     \\\\\n",
       "\\textbf{C(strike)[7]}   &      34.9706  &        2.345     &    14.913  &         0.000        &       30.375    &       39.567     \\\\\n",
       "\\textbf{C(strike)[8]}   &      -2.2891  &        2.441     &    -0.938  &         0.348        &       -7.074    &        2.495     \\\\\n",
       "\\textbf{C(strike)[11]}  &       6.4909  &        2.201     &     2.949  &         0.003        &        2.178    &       10.804     \\\\\n",
       "\\textbf{C(strike)[12]}  &       4.3378  &        2.644     &     1.641  &         0.101        &       -0.844    &        9.520     \\\\\n",
       "\\textbf{C(strike)[14]}  &      13.6676  &        2.352     &     5.811  &         0.000        &        9.058    &       18.277     \\\\\n",
       "\\textbf{C(strike)[20]}  &      20.1515  &        2.184     &     9.226  &         0.000        &       15.870    &       24.433     \\\\\n",
       "\\textbf{C(strike)[24]}  &      53.5148  &        2.366     &    22.614  &         0.000        &       48.877    &       58.153     \\\\\n",
       "\\textbf{C(strike)[26]}  &      22.5152  &        2.395     &     9.402  &         0.000        &       17.822    &       27.209     \\\\\n",
       "\\textbf{C(strike)[27]}  &      23.9252  &        2.317     &    10.325  &         0.000        &       19.383    &       28.467     \\\\\n",
       "\\textbf{C(strike)[28]}  &      15.0769  &        2.253     &     6.691  &         0.000        &       10.660    &       19.493     \\\\\n",
       "\\textbf{C(strike)[34]}  &       5.0173  &        2.409     &     2.082  &         0.037        &        0.295    &        9.740     \\\\\n",
       "\\textbf{C(strike)[35]}  &       4.1133  &        2.465     &     1.669  &         0.095        &       -0.717    &        8.944     \\\\\n",
       "\\textbf{C(strike)[37]}  &      -0.0476  &        2.468     &    -0.019  &         0.985        &       -4.885    &        4.790     \\\\\n",
       "\\textbf{C(strike)[38]}  &       1.5949  &        2.380     &     0.670  &         0.503        &       -3.071    &        6.260     \\\\\n",
       "\\textbf{C(strike)[40]}  &      21.4962  &        2.461     &     8.734  &         0.000        &       16.672    &       26.320     \\\\\n",
       "\\textbf{C(strike)[42]}  &      12.4249  &        2.421     &     5.133  &         0.000        &        7.680    &       17.169     \\\\\n",
       "\\textbf{C(strike)[43]}  &      14.5757  &        2.270     &     6.420  &         0.000        &       10.126    &       19.026     \\\\\n",
       "\\textbf{C(strike)[48]}  &      17.1657  &        2.397     &     7.162  &         0.000        &       12.468    &       21.863     \\\\\n",
       "\\textbf{C(strike)[51]}  &      53.7898  &        2.365     &    22.741  &         0.000        &       49.154    &       58.426     \\\\\n",
       "\\textbf{C(strike)[58]}  &      18.1732  &        2.228     &     8.155  &         0.000        &       13.805    &       22.541     \\\\\n",
       "\\textbf{C(strike)[59]}  &      10.1090  &        2.491     &     4.059  &         0.000        &        5.228    &       14.990     \\\\\n",
       "\\textbf{C(strike)[60]}  &      16.4680  &        2.236     &     7.363  &         0.000        &       12.085    &       20.851     \\\\\n",
       "\\textbf{C(strike)[62]}  &      37.0450  &        2.378     &    15.576  &         0.000        &       32.384    &       41.706     \\\\\n",
       "\\textbf{C(strike)[65]}  &      53.1275  &        2.347     &    22.633  &         0.000        &       48.527    &       57.728     \\\\\n",
       "\\textbf{C(strike)[67]}  &      13.3357  &        2.196     &     6.072  &         0.000        &        9.031    &       17.640     \\\\\n",
       "\\textbf{C(strike)[68]}  &      16.8176  &        2.164     &     7.771  &         0.000        &       12.576    &       21.059     \\\\\n",
       "\\textbf{C(strike)[71]}  &      62.8984  &        2.318     &    27.137  &         0.000        &       58.356    &       67.441     \\\\\n",
       "\\textbf{C(strike)[81]}  &      27.4297  &        2.365     &    11.598  &         0.000        &       22.794    &       32.065     \\\\\n",
       "\\textbf{C(strike)[82]}  &      31.3934  &        2.248     &    13.963  &         0.000        &       26.987    &       35.800     \\\\\n",
       "\\textbf{C(strike)[83]}  &      20.4277  &        2.227     &     9.175  &         0.000        &       16.064    &       24.792     \\\\\n",
       "\\textbf{C(strike)[85]}  &      29.4599  &        2.248     &    13.104  &         0.000        &       25.053    &       33.866     \\\\\n",
       "\\textbf{C(strike)[87]}  &       3.7434  &        1.991     &     1.880  &         0.060        &       -0.159    &        7.645     \\\\\n",
       "\\textbf{C(strike)[92]}  &      13.7049  &        2.310     &     5.932  &         0.000        &        9.177    &       18.233     \\\\\n",
       "\\textbf{C(strike)[95]}  &      56.5506  &        1.932     &    29.277  &         0.000        &       52.765    &       60.336     \\\\\n",
       "\\textbf{C(strike)[100]} &      86.0565  &        2.341     &    36.767  &         0.000        &       81.469    &       90.644     \\\\\n",
       "\\textbf{C(strike)[106]} &       4.4099  &        1.616     &     2.729  &         0.006        &        1.243    &        7.577     \\\\\n",
       "\\textbf{C(strike)[107]} &      48.0058  &        1.555     &    30.867  &         0.000        &       44.958    &       51.054     \\\\\n",
       "\\textbf{X\\_1}           &       0.3074  &        1.007     &     0.305  &         0.760        &       -1.666    &        2.281     \\\\\n",
       "\\textbf{X\\_2}           &       1.1458  &        1.787     &     0.641  &         0.521        &       -2.357    &        4.649     \\\\\n",
       "\\textbf{X\\_3}           &      -0.2222  &        1.287     &    -0.173  &         0.863        &       -2.744    &        2.300     \\\\\n",
       "\\textbf{X\\_4}           &      -0.7810  &        1.331     &    -0.587  &         0.557        &       -3.389    &        1.827     \\\\\n",
       "\\textbf{X\\_5}           &      -0.6583  &        1.560     &    -0.422  &         0.673        &       -3.716    &        2.399     \\\\\n",
       "\\textbf{X\\_7}           &       0.6975  &        1.799     &     0.388  &         0.698        &       -2.829    &        4.224     \\\\\n",
       "\\textbf{X\\_8}           &      -0.6186  &        1.811     &    -0.342  &         0.733        &       -4.168    &        2.931     \\\\\n",
       "\\textbf{X\\_9}           &       5.2860  &        2.195     &     2.409  &         0.016        &        0.985    &        9.587     \\\\\n",
       "\\textbf{X\\_10}          &       6.4273  &        2.220     &     2.895  &         0.004        &        2.075    &       10.779     \\\\\n",
       "\\textbf{X\\_11}          &       8.1163  &        2.443     &     3.323  &         0.001        &        3.329    &       12.904     \\\\\n",
       "\\textbf{X\\_12}          &      10.5316  &        2.620     &     4.020  &         0.000        &        5.397    &       15.666     \\\\\n",
       "\\textbf{X\\_13}          &       9.5368  &        2.607     &     3.659  &         0.000        &        4.428    &       14.646     \\\\\n",
       "\\textbf{X\\_14}          &      12.8704  &        3.280     &     3.924  &         0.000        &        6.442    &       19.298     \\\\\n",
       "\\textbf{X\\_15}          &      11.8915  &        2.535     &     4.690  &         0.000        &        6.922    &       16.861     \\\\\n",
       "\\textbf{X\\_16}          &      12.1660  &        3.121     &     3.898  &         0.000        &        6.049    &       18.284     \\\\\n",
       "\\textbf{X\\_17}          &      14.6754  &        3.180     &     4.615  &         0.000        &        8.442    &       20.909     \\\\\n",
       "\\textbf{X\\_18}          &      13.9034  &        3.481     &     3.994  &         0.000        &        7.081    &       20.726     \\\\\n",
       "\\textbf{X\\_19}          &      11.0857  &        2.253     &     4.922  &         0.000        &        6.671    &       15.501     \\\\\n",
       "\\textbf{X\\_20}          &      12.9464  &        2.744     &     4.718  &         0.000        &        7.568    &       18.325     \\\\\n",
       "\\textbf{X\\_21}          &      13.3436  &        2.505     &     5.328  &         0.000        &        8.435    &       18.252     \\\\\n",
       "\\textbf{X\\_22}          &      15.8124  &        2.856     &     5.536  &         0.000        &       10.215    &       21.410     \\\\\n",
       "\\textbf{X\\_23}          &      16.1379  &        3.128     &     5.158  &         0.000        &       10.006    &       22.270     \\\\\n",
       "\\textbf{X\\_24}          &      16.9843  &        2.677     &     6.344  &         0.000        &       11.737    &       22.232     \\\\\n",
       "\\textbf{X\\_25}          &      18.1176  &        2.936     &     6.171  &         0.000        &       12.363    &       23.872     \\\\\n",
       "\\textbf{X\\_26}          &      16.6632  &        2.652     &     6.284  &         0.000        &       11.466    &       21.860     \\\\\n",
       "\\textbf{X\\_27}          &      17.9490  &        2.176     &     8.249  &         0.000        &       13.684    &       22.214     \\\\\n",
       "\\textbf{X\\_28}          &      18.5940  &        2.508     &     7.413  &         0.000        &       13.678    &       23.510     \\\\\n",
       "\\textbf{X\\_29}          &      20.2843  &        2.695     &     7.527  &         0.000        &       15.003    &       25.566     \\\\\n",
       "\\textbf{X\\_30}          &      16.9527  &        2.724     &     6.223  &         0.000        &       11.613    &       22.292     \\\\\n",
       "\\textbf{X\\_31}          &      18.7964  &        3.024     &     6.216  &         0.000        &       12.870    &       24.723     \\\\\n",
       "\\textbf{X\\_32}          &      19.1026  &        3.318     &     5.757  &         0.000        &       12.600    &       25.605     \\\\\n",
       "\\textbf{X\\_33}          &      20.8021  &        2.806     &     7.413  &         0.000        &       15.302    &       26.302     \\\\\n",
       "\\textbf{X\\_34}          &      17.7033  &        2.834     &     6.248  &         0.000        &       12.150    &       23.257     \\\\\n",
       "\\textbf{X\\_35}          &      20.8472  &        3.153     &     6.612  &         0.000        &       14.667    &       27.027     \\\\\n",
       "\\textbf{X\\_36}          &      20.3511  &        3.740     &     5.442  &         0.000        &       13.022    &       27.681     \\\\\n",
       "\\textbf{X\\_37}          &      26.8554  &        3.377     &     7.953  &         0.000        &       20.237    &       33.474     \\\\\n",
       "\\textbf{X\\_38}          &      20.9924  &        4.160     &     5.046  &         0.000        &       12.839    &       29.146     \\\\\n",
       "\\textbf{X\\_39}          &      21.5411  &        3.781     &     5.698  &         0.000        &       14.131    &       28.951     \\\\\n",
       "\\textbf{X\\_40}          &      24.9044  &        4.649     &     5.357  &         0.000        &       15.793    &       34.016     \\\\\n",
       "\\textbf{X\\_41}          &      23.3910  &        4.569     &     5.120  &         0.000        &       14.436    &       32.346     \\\\\n",
       "\\textbf{X\\_42}          &      25.8306  &        4.796     &     5.386  &         0.000        &       16.430    &       35.231     \\\\\n",
       "\\textbf{X\\_43}          &      24.4808  &        3.802     &     6.439  &         0.000        &       17.029    &       31.933     \\\\\n",
       "\\textbf{X\\_44}          &      20.3821  &        3.777     &     5.397  &         0.000        &       12.980    &       27.784     \\\\\n",
       "\\textbf{X\\_45}          &      28.1470  &        2.950     &     9.542  &         0.000        &       22.365    &       33.929     \\\\\n",
       "\\textbf{X\\_46}          &      24.4605  &        3.798     &     6.440  &         0.000        &       17.016    &       31.905     \\\\\n",
       "\\textbf{X\\_47}          &      24.1590  &        4.586     &     5.268  &         0.000        &       15.171    &       33.147     \\\\\n",
       "\\textbf{X\\_48}          &      23.4124  &        4.536     &     5.161  &         0.000        &       14.522    &       32.303     \\\\\n",
       "\\textbf{X\\_49}          &      24.3156  &        4.196     &     5.794  &         0.000        &       16.091    &       32.540     \\\\\n",
       "\\textbf{X\\_50}          &      22.4903  &        4.283     &     5.251  &         0.000        &       14.095    &       30.885     \\\\\n",
       "\\textbf{X\\_51}          &      21.5832  &        4.168     &     5.179  &         0.000        &       13.415    &       29.752     \\\\\n",
       "\\textbf{X\\_52}          &      23.1871  &        4.476     &     5.180  &         0.000        &       14.413    &       31.961     \\\\\n",
       "\\textbf{X\\_53}          &      23.8819  &        3.623     &     6.593  &         0.000        &       16.782    &       30.982     \\\\\n",
       "\\textbf{X\\_54}          &      23.5255  &        4.479     &     5.253  &         0.000        &       14.748    &       32.303     \\\\\n",
       "\\textbf{X\\_55}          &      22.1215  &        4.070     &     5.435  &         0.000        &       14.145    &       30.098     \\\\\n",
       "\\textbf{X\\_56}          &      25.3757  &        5.230     &     4.852  &         0.000        &       15.125    &       35.626     \\\\\n",
       "\\textbf{X\\_57}          &      21.6280  &        4.395     &     4.921  &         0.000        &       13.014    &       30.242     \\\\\n",
       "\\textbf{X\\_58}          &      23.1101  &        3.893     &     5.936  &         0.000        &       15.479    &       30.741     \\\\\n",
       "\\textbf{X\\_59}          &      20.9405  &        3.566     &     5.872  &         0.000        &       13.951    &       27.930     \\\\\n",
       "\\textbf{X\\_60}          &      21.7947  &        4.031     &     5.407  &         0.000        &       13.895    &       29.694     \\\\\n",
       "\\textbf{X\\_61}          &      20.9569  &        3.933     &     5.329  &         0.000        &       13.249    &       28.665     \\\\\n",
       "\\textbf{X\\_62}          &      22.8382  &        3.659     &     6.242  &         0.000        &       15.667    &       30.010     \\\\\n",
       "\\textbf{X\\_63}          &      23.1336  &        3.025     &     7.647  &         0.000        &       17.205    &       29.063     \\\\\n",
       "\\textbf{X\\_64}          &      28.2942  &        3.446     &     8.210  &         0.000        &       21.540    &       35.049     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 353610.898 & \\textbf{  Durbin-Watson:     } &      1.975    \\\\\n",
       "\\textbf{Prob(Omnibus):} &    0.000   & \\textbf{  Jarque-Bera (JB):  } & 15889168.047  \\\\\n",
       "\\textbf{Skew:}          &    4.560   & \\textbf{  Prob(JB):          } &       0.00    \\\\\n",
       "\\textbf{Kurtosis:}      &   33.419   & \\textbf{  Cond. No.          } &       34.5    \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors are robust to cluster correlation (cluster)"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:               distance   R-squared:                       0.047\n",
       "Model:                            OLS   Adj. R-squared:                  0.046\n",
       "Method:                 Least Squares   F-statistic:                       nan\n",
       "Date:                Wed, 21 May 2025   Prob (F-statistic):                nan\n",
       "Time:                        12:45:56   Log-Likelihood:            -2.1675e+06\n",
       "No. Observations:              378132   AIC:                         4.335e+06\n",
       "Df Residuals:                  378031   BIC:                         4.336e+06\n",
       "Df Model:                         100                                         \n",
       "Covariance Type:              cluster                                         \n",
       "==================================================================================\n",
       "                     coef    std err          z      P>|z|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------\n",
       "C(strike)[1]       8.6841      2.399      3.620      0.000       3.983      13.386\n",
       "C(strike)[7]      34.9706      2.345     14.913      0.000      30.375      39.567\n",
       "C(strike)[8]      -2.2891      2.441     -0.938      0.348      -7.074       2.495\n",
       "C(strike)[11]      6.4909      2.201      2.949      0.003       2.178      10.804\n",
       "C(strike)[12]      4.3378      2.644      1.641      0.101      -0.844       9.520\n",
       "C(strike)[14]     13.6676      2.352      5.811      0.000       9.058      18.277\n",
       "C(strike)[20]     20.1515      2.184      9.226      0.000      15.870      24.433\n",
       "C(strike)[24]     53.5148      2.366     22.614      0.000      48.877      58.153\n",
       "C(strike)[26]     22.5152      2.395      9.402      0.000      17.822      27.209\n",
       "C(strike)[27]     23.9252      2.317     10.325      0.000      19.383      28.467\n",
       "C(strike)[28]     15.0769      2.253      6.691      0.000      10.660      19.493\n",
       "C(strike)[34]      5.0173      2.409      2.082      0.037       0.295       9.740\n",
       "C(strike)[35]      4.1133      2.465      1.669      0.095      -0.717       8.944\n",
       "C(strike)[37]     -0.0476      2.468     -0.019      0.985      -4.885       4.790\n",
       "C(strike)[38]      1.5949      2.380      0.670      0.503      -3.071       6.260\n",
       "C(strike)[40]     21.4962      2.461      8.734      0.000      16.672      26.320\n",
       "C(strike)[42]     12.4249      2.421      5.133      0.000       7.680      17.169\n",
       "C(strike)[43]     14.5757      2.270      6.420      0.000      10.126      19.026\n",
       "C(strike)[48]     17.1657      2.397      7.162      0.000      12.468      21.863\n",
       "C(strike)[51]     53.7898      2.365     22.741      0.000      49.154      58.426\n",
       "C(strike)[58]     18.1732      2.228      8.155      0.000      13.805      22.541\n",
       "C(strike)[59]     10.1090      2.491      4.059      0.000       5.228      14.990\n",
       "C(strike)[60]     16.4680      2.236      7.363      0.000      12.085      20.851\n",
       "C(strike)[62]     37.0450      2.378     15.576      0.000      32.384      41.706\n",
       "C(strike)[65]     53.1275      2.347     22.633      0.000      48.527      57.728\n",
       "C(strike)[67]     13.3357      2.196      6.072      0.000       9.031      17.640\n",
       "C(strike)[68]     16.8176      2.164      7.771      0.000      12.576      21.059\n",
       "C(strike)[71]     62.8984      2.318     27.137      0.000      58.356      67.441\n",
       "C(strike)[81]     27.4297      2.365     11.598      0.000      22.794      32.065\n",
       "C(strike)[82]     31.3934      2.248     13.963      0.000      26.987      35.800\n",
       "C(strike)[83]     20.4277      2.227      9.175      0.000      16.064      24.792\n",
       "C(strike)[85]     29.4599      2.248     13.104      0.000      25.053      33.866\n",
       "C(strike)[87]      3.7434      1.991      1.880      0.060      -0.159       7.645\n",
       "C(strike)[92]     13.7049      2.310      5.932      0.000       9.177      18.233\n",
       "C(strike)[95]     56.5506      1.932     29.277      0.000      52.765      60.336\n",
       "C(strike)[100]    86.0565      2.341     36.767      0.000      81.469      90.644\n",
       "C(strike)[106]     4.4099      1.616      2.729      0.006       1.243       7.577\n",
       "C(strike)[107]    48.0058      1.555     30.867      0.000      44.958      51.054\n",
       "X_1                0.3074      1.007      0.305      0.760      -1.666       2.281\n",
       "X_2                1.1458      1.787      0.641      0.521      -2.357       4.649\n",
       "X_3               -0.2222      1.287     -0.173      0.863      -2.744       2.300\n",
       "X_4               -0.7810      1.331     -0.587      0.557      -3.389       1.827\n",
       "X_5               -0.6583      1.560     -0.422      0.673      -3.716       2.399\n",
       "X_7                0.6975      1.799      0.388      0.698      -2.829       4.224\n",
       "X_8               -0.6186      1.811     -0.342      0.733      -4.168       2.931\n",
       "X_9                5.2860      2.195      2.409      0.016       0.985       9.587\n",
       "X_10               6.4273      2.220      2.895      0.004       2.075      10.779\n",
       "X_11               8.1163      2.443      3.323      0.001       3.329      12.904\n",
       "X_12              10.5316      2.620      4.020      0.000       5.397      15.666\n",
       "X_13               9.5368      2.607      3.659      0.000       4.428      14.646\n",
       "X_14              12.8704      3.280      3.924      0.000       6.442      19.298\n",
       "X_15              11.8915      2.535      4.690      0.000       6.922      16.861\n",
       "X_16              12.1660      3.121      3.898      0.000       6.049      18.284\n",
       "X_17              14.6754      3.180      4.615      0.000       8.442      20.909\n",
       "X_18              13.9034      3.481      3.994      0.000       7.081      20.726\n",
       "X_19              11.0857      2.253      4.922      0.000       6.671      15.501\n",
       "X_20              12.9464      2.744      4.718      0.000       7.568      18.325\n",
       "X_21              13.3436      2.505      5.328      0.000       8.435      18.252\n",
       "X_22              15.8124      2.856      5.536      0.000      10.215      21.410\n",
       "X_23              16.1379      3.128      5.158      0.000      10.006      22.270\n",
       "X_24              16.9843      2.677      6.344      0.000      11.737      22.232\n",
       "X_25              18.1176      2.936      6.171      0.000      12.363      23.872\n",
       "X_26              16.6632      2.652      6.284      0.000      11.466      21.860\n",
       "X_27              17.9490      2.176      8.249      0.000      13.684      22.214\n",
       "X_28              18.5940      2.508      7.413      0.000      13.678      23.510\n",
       "X_29              20.2843      2.695      7.527      0.000      15.003      25.566\n",
       "X_30              16.9527      2.724      6.223      0.000      11.613      22.292\n",
       "X_31              18.7964      3.024      6.216      0.000      12.870      24.723\n",
       "X_32              19.1026      3.318      5.757      0.000      12.600      25.605\n",
       "X_33              20.8021      2.806      7.413      0.000      15.302      26.302\n",
       "X_34              17.7033      2.834      6.248      0.000      12.150      23.257\n",
       "X_35              20.8472      3.153      6.612      0.000      14.667      27.027\n",
       "X_36              20.3511      3.740      5.442      0.000      13.022      27.681\n",
       "X_37              26.8554      3.377      7.953      0.000      20.237      33.474\n",
       "X_38              20.9924      4.160      5.046      0.000      12.839      29.146\n",
       "X_39              21.5411      3.781      5.698      0.000      14.131      28.951\n",
       "X_40              24.9044      4.649      5.357      0.000      15.793      34.016\n",
       "X_41              23.3910      4.569      5.120      0.000      14.436      32.346\n",
       "X_42              25.8306      4.796      5.386      0.000      16.430      35.231\n",
       "X_43              24.4808      3.802      6.439      0.000      17.029      31.933\n",
       "X_44              20.3821      3.777      5.397      0.000      12.980      27.784\n",
       "X_45              28.1470      2.950      9.542      0.000      22.365      33.929\n",
       "X_46              24.4605      3.798      6.440      0.000      17.016      31.905\n",
       "X_47              24.1590      4.586      5.268      0.000      15.171      33.147\n",
       "X_48              23.4124      4.536      5.161      0.000      14.522      32.303\n",
       "X_49              24.3156      4.196      5.794      0.000      16.091      32.540\n",
       "X_50              22.4903      4.283      5.251      0.000      14.095      30.885\n",
       "X_51              21.5832      4.168      5.179      0.000      13.415      29.752\n",
       "X_52              23.1871      4.476      5.180      0.000      14.413      31.961\n",
       "X_53              23.8819      3.623      6.593      0.000      16.782      30.982\n",
       "X_54              23.5255      4.479      5.253      0.000      14.748      32.303\n",
       "X_55              22.1215      4.070      5.435      0.000      14.145      30.098\n",
       "X_56              25.3757      5.230      4.852      0.000      15.125      35.626\n",
       "X_57              21.6280      4.395      4.921      0.000      13.014      30.242\n",
       "X_58              23.1101      3.893      5.936      0.000      15.479      30.741\n",
       "X_59              20.9405      3.566      5.872      0.000      13.951      27.930\n",
       "X_60              21.7947      4.031      5.407      0.000      13.895      29.694\n",
       "X_61              20.9569      3.933      5.329      0.000      13.249      28.665\n",
       "X_62              22.8382      3.659      6.242      0.000      15.667      30.010\n",
       "X_63              23.1336      3.025      7.647      0.000      17.205      29.063\n",
       "X_64              28.2942      3.446      8.210      0.000      21.540      35.049\n",
       "==============================================================================\n",
       "Omnibus:                   353610.898   Durbin-Watson:                   1.975\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):         15889168.047\n",
       "Skew:                           4.560   Prob(JB):                         0.00\n",
       "Kurtosis:                      33.419   Cond. No.                         34.5\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors are robust to cluster correlation (cluster)\n",
       "\"\"\""
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# run the event study regression for distance around strikes\n",
    "# 7 lags and 56 leads\n",
    "# time vars provide the parameters of interest\n",
    "# drop the day = -2 variable for difference in means and to avoid multicollinearity\n",
    "# use strike fixed effects and cluster SE by strike\n",
    "reg = sm.ols('distance ~ ' + var_form + ' + C(strike) - 1', \n",
    "             data=df_low).fit(cov_type='cluster',\n",
    "                          cov_kwds={'groups': np.array(df_low[['strike']])})\n",
    "\n",
    "\n",
    "# save parameters and confidence intervals for plotting\n",
    "conf_int = reg.conf_int()\n",
    "res_lowdensity = pd.DataFrame({\n",
    "    'params': reg.params,\n",
    "    'ci_l': conf_int[0],  # lower bound\n",
    "    'ci_h': conf_int[1],  # upper bound\n",
    "})\n",
    "\n",
    "# print regression summary\n",
    "reg.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "event_res_highdensity = res_highdensity[res_highdensity.index.to_series().str.startswith(\"X_\")].copy()\n",
    "event_res_highdensity = event_res_highdensity.reset_index().rename(columns={'index': 'varname'})\n",
    "event_res_highdensity['day'] = event_res_highdensity['varname'].str.extract(r'X_(\\d+)').astype(float).astype('Int64') - 8\n",
    "\n",
    "event_res_lowdensity = res_lowdensity[res_lowdensity.index.to_series().str.startswith(\"X_\")].copy()\n",
    "event_res_lowdensity = event_res_lowdensity.reset_index().rename(columns={'index': 'varname'})\n",
    "event_res_lowdensity['day'] = event_res_lowdensity['varname'].str.extract(r'X_(\\d+)').astype(float).astype('Int64') - 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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88krccMMNeOGFF3DnnXfi+uuv95VnBVNcXIz58+dj+vTpEAQBDz30UMwXrU9NTcWNN96Iu+++Gzk5OejZsyeeffZZ2Gw2XHPNNX73Dfa6TZw4ERUVFXj22Wdx4YUXYtGiRfjf//6HjIyMgPvNzs5Gly5d8NZbb6GwsBAHDx5s1ewiLy8PZrMZixYtQo8ePWAymVq9v5SMPxK1tbUoKyuD0+nEzp078eabb+LLL7/EBx984OtYeO+99+L444/HzTffjOuuuw6pqanYtm0blixZgn/9619h7eell15CYWEhRo0aBY1Gg88++wwFBQUB26AHe16mTp2KzMxMzJ49G4899ljUv384OB3AGGOMMdaGd999F6effnqrg1iAMkobN27Er7/+6rvu6aefxm233YZjjjkGpaWlWLBgge8s+ZgxY/Dpp5/i448/xrBhw/Dwww/jsccea7XY60UXXYSqqirYbDa/VtEAnVV/5513MHfuXAwfPhwTJkzA3LlzfaVS4cjIyGjzIB+ghhEPP/wwnnrqKQwePBhTp07F119/7dtHz5498fnnn+Prr7/GyJEj8cYbb+DJJ58Mus+XXnoJ2dnZOPHEEzF9+nRMnToVY8aMCXvMkXr66adxwQUX4IorrsCYMWOwe/duLF68uFW5ZLDXbfDgwXjttdfw73//GyNHjsTatWuDrtOk0Wjw8ccfY8OGDRg2bBjuuOMOPPfcc3730el0eOWVV/Dmm2+iW7duOPfcc6MafySuuuoqFBYWYtCgQbjxxhuRlpaGtWvX+rJXAM09WrlyJXbt2oVTTjkFo0ePxkMPPeSbBxWOtLQ0PPPMMzj22GMxduxY7N+/HwsXLgxYlRTsedFoNJg5cyZEUcRf/vKX6H75MAlyJIWm7Uh9fT0yMzNRV1cX9EOBhU+WZV/db2FhYVh1vowxxpjD4cC+ffvQp08fxd24GIuFFStWYNKkSaipqYnbQq8sctdddx2OHj2KBQsWBL1fsM8aJbEBl94xxSRJwoYNGwBQO8hwJ5AyxhhjjDGmVF1dHdatW4d58+bhq6++itt+OVBijDHGGGOMJa1zzz0Xa9euxfXXX+9b7yoeOFBijDHGGGOd0sSJEyNqd87iKx6twAPhZg6MMcYYY4wx1gIHSowxxhhjjDHWAgdKjDHGGGOMMdYCB0qMMcYYY4wx1gI3c2CKCYKAUaNG+S4zxhhjjDHW0XCgxBTTaDQoKipK9DAYY4wxxhiLGS69Y4wxxhiLg5kzZ+K8885L9DBUJwgCvvzySwDA/v37IQgCNm7cmNAxxcvcuXORlZWVNNtpTyZOnIjbb7890cMIigMlppgsyzh69CiOHj3Kaw8wxhjr8GbOnAlBEHxfXbp0wRlnnIFNmzYlemhBzZ07F4IgYPDgwa1u+/TTTyEIAnr37q3qPouKilBaWophw4aput2WvL+b96uwsBAXX3wx9u3bF9P9qqF37954+eWX/a675JJLsHPnzpjve+LEib7nzGg0onv37pg+fTrmz58f8323NH/+fDz++OO+nwM9L4nGgRJTTJIkrF27FmvXroUkSYkeDmOMMRZzZ5xxBkpLS1FaWoply5ZBp9Ph7LPPTvSwQkpNTUV5eTl+/vlnv+vfe+899OzZU/X9abVaFBQUQKeL/eyOjIwMlJaW4siRI/jwww+xceNGnHPOORBFMeb7VpvZbEZeXl5c9nXdddehtLQUu3fvxueff44hQ4bgT3/6E/7617/GZf9eOTk5SE9Pj+s+leJAiTHGGGMsBKPRiIKCAhQUFGDUqFG49957UVJSgoqKCt99Dh8+jEsuuQTZ2dno0qULzj33XOzfv7/NbTqdTtx6663Iy8uDyWTCySefjHXr1vluP+aYY/DCCy/4fj7vvPOg0+lQX18PACgrK4MgCNixY0eb+9DpdLj00kvx3nvv+a47dOgQVqxYgUsvvbTV/b/++mscc8wxMJlM6Nu3Lx599FF4PB7f7bt27cL48eNhMpkwZMgQLFmyxO/xLUvvRFHENddcgz59+sBsNmPgwIH45z//6fcYb0ni888/j8LCQnTp0gU333wz3G53m78XQCV/BQUFKCwsxKRJk/DII4/gjz/+wO7duwEAr7/+Ovr16weDwYCBAwfi//7v/1o9/vXXX8eZZ54Js9mMPn364LPPPvPdvmLFCgiCgNraWt91GzduhCAIbb6ue/bswbnnnov8/HykpaVh7NixWLp0qe/2iRMn4sCBA7jjjjt8mR0gcOldOON/5513cP755yMlJQX9+/fHggULgj5nAJCSkoKCggIUFRXh+OOPxzPPPIM333wTb7/9tt9YQ72fw3ndXnvtNfTv3x8mkwn5+fm48MIL/Z4Lb+ldoOfFarUiIyMD//3vf/3G//XXXyM1NRUNDQ0hf9docaDEGGOMscSQZcBlTcxXFKXjFosF8+bNQ3FxMbp06QIAsNlsmDRpEtLS0vDDDz/gxx9/RFpaGs444wy4XK6A27nnnnvw+eef4/3338evv/6K4uJiTJ06FdXV1QDo4HHFihWNT5WMVatWITs7Gz/++CMAYPny5SgoKMDAgQODjveaa67BJ598ApvNBoAOys844wzk5+f73W/x4sW4/PLLceutt2Lr1q148803MXfuXDzxxBMAqKJkxowZ0Gq1WLNmDd544w3ce++9QfctSRJ69OiBTz/9FFu3bsXDDz+MBx54AJ9++qnf/ZYvX449e/Zg+fLleP/99zF37lzMnTs36LZbMpvNAAC3240vvvgCt912G/7+97/jjz/+wPXXX4+rrroKy5cv93vMQw89hAsuuAC///47Lr/8cvz5z3/Gtm3bFO23OYvFgmnTpmHp0qX47bffMHXqVEyfPh0HDx4EQOVmPXr0wGOPPebLUAYS7vgfffRRXHzxxdi0aROmTZuGyy67zPf+UeLKK69Edna2rwQv3PdzsNdt/fr1uPXWW/HYY49hx44dWLRoEcaPHx9w/4Gel9TUVPzpT3/CnDlz/O47Z84cXHjhhXHJRnHXO8YYY4wlhtsGPNktMft+4AhgSA377t988w3S0tIAAFarFYWFhfjmm2+g0dA5548//hgajQbvvPOOL0swZ84cZGVlYcWKFZgyZYrf9qxWK15//XXMnTsXZ555JgDg7bffxpIlS/Duu+/i7rvvxsSJE/Huu+9CkiRs3rwZWq0Wl19+OVasWIFp06ZhxYoVmDBhQsixjxo1Cv369cN///tfXHHFFZg7dy5efPFF7N271+9+TzzxBO677z5ceeWVAIC+ffvi8ccfxz333INHHnkES5cuxbZt27B//3706NEDAPDkk0/6xh+IXq/Ho48+6vu5T58+WL16NT799FNcfPHFvuuzs7Px6quvQqvVYtCgQTjrrLOwbNkyXHfddSF/P4CyZM899xx69OiBAQMG4Prrr8fMmTNx0003AQDuvPNOrFmzBs8//zwmTZrke9xFF12Ea6+9FgDw+OOPY8mSJfjXv/6F1157Laz9tjRy5EiMHDnS9/Ps2bPxxRdfYMGCBbjllluQk5MDrVaL9PR0FBQUtLmd559/Pqzxz5w5E3/+858B0Gvxr3/9C2vXrsUZZ5yhaNwajQYDBgzwZYzCfT8He90OHjyI1NRUnH322UhPT0evXr0wevTogPtv63m59tprceKJJ+LIkSPo1q0bKisr8c0337TKZMYKZ5QYY4wxxkKYNGkSNm7ciI0bN+KXX37BlClTcOaZZ+LAgQMAgA0bNmD37t1IT09HWloa0tLSkJOTA4fDgT179rTa3p49e+B2u3HSSSf5rtPr9TjuuON8GY3x48ejoaEBv/32G1auXIkJEyZg0qRJWLlyJQCEHSgBwNVXX405c+Zg5cqVvqxHSxs2bMBjjz3mG39aWppvPovNZsO2bdvQs2dPX5AEACeccELIfb/xxhs49thj0bVrV6SlpeHtt9/2ZVi8hg4dCq1W6/u5sLAQ5eXlQbdbV1eHtLQ0pKamoqioCC6XC/Pnz4fBYMC2bdv8nlsAOOmkk1pli1qO/4QTTogqo2S1WnHPPfdgyJAhyMrKQlpaGrZv397q9w0l3PGPGDHCdzk1NRXp6ekhn7e2yLLsC4rCfT8He90mT56MXr16oW/fvrjiiiswb948X1YzXMcddxyGDh2KDz74AADwf//3f+jZs2ebmSm1JU1G6amnnsIDDzyA2267zdfxQpZlPProo3jrrbdQU1ODcePG4d///jeGDh2a2MEyxhhjLHr6FMrsJGrfCqSmpqK4uNj38zHHHIPMzEy8/fbbmD17NiRJwjHHHIN58+a1emzXrl1bXeftGtty4fbmB6uZmZkYNWoUVqxYgdWrV+PUU0/FKaecgo0bN2LXrl3YuXMnJk6cGNb4L7vsMtxzzz2YNWsW/vKXvwRstiBJEh599FHMmDGj1W0mkylgp9tQC89/+umnuOOOO/DCCy/ghBNOQHp6Op577jn88ssvfvfT6/WtthuqYVR6ejp+/fVXaDQa5OfnIzXVP0MY7LkNxnsfb7aw+e8dat7U3XffjcWLF+P5559HcXExzGYzLrzwwjbLL8MZh1eg8UfyvAUiiiJ27dqFsWPHAkDY7+dg+/e+PitWrMB3332Hhx9+GLNmzcK6desUtUK/9tpr8eqrr+K+++7DnDlzcNVVV4X1OqohKTJK69atw1tvveUXFQPAs88+ixdffBGvvvoq1q1bh4KCAkyePDkuk7cYY4wxFmOCQOVvifiK8kBLEARoNBrY7XYAwJgxY7Br1y7k5eWhuLjY7yszM7PV44uLi2EwGHzzjQA6CF+/fr1fO++JEydi+fLl+OGHHzBx4kRkZWVhyJAhmD17NvLy8gK2/g4kJycH55xzDlauXImrr7464H3GjBmDHTt2tBp/cXExNBoNhgwZgoMHD+LIkabgtmU3vZZWrVqFE088ETfddBNGjx6N4uLigBm2SGg0GhQXF6Nv376tgqTBgwf7PbcAsHr16lbP15o1a1r9PGjQIABNAUHzeUSh1odatWoVZs6cifPPPx/Dhw9HQUFBq8YPBoMhZGe+cMevlvfffx81NTW44IILACh/P7dFp9Ph9NNPx7PPPotNmzZh//79+P777wPet63n5fLLL8fBgwfxyiuvYMuWLb7S0HhIeKBksVhw2WWX4e2330Z2drbvelmW8fLLL+PBBx/EjBkzMGzYMLz//vuw2Wz48MMPEzhiJggChg8fjuHDh8ctomeMMcYSyel0oqysDGVlZdi2bRv+9re/wWKxYPr06QAoY5Obm4tzzz0Xq1atwr59+7By5UrcdtttOHToUKvtpaam4sYbb8Tdd9+NRYsWYevWrbjuuutgs9lwzTXX+O43ceJELFq0CIIgYMiQIb7r5s2bF3bZndfcuXNRWVnpCwRaevjhh/HBBx9g1qxZ2LJlC7Zt24ZPPvkE//jHPwAAp59+OgYOHIi//OUv+P3337Fq1So8+OCDQfdZXFyM9evXY/Hixdi5cyceeughv85+sXL33Xdj7ty5eOONN7Br1y68+OKLmD9/Pu666y6/+3322Wd47733sHPnTjzyyCNYu3YtbrnlFt/Yi4qKMGvWLOzcuRPffvutXxfCQIqLizF//nxs3LgRv//+Oy699NJWGZ7evXvjhx9+wOHDh1FZWRnV+CNhs9lQVlaGQ4cO4ZdffsG9996LG264ATfeeKNv/pPS93Mg33zzDV555RVs3LgRBw4cwAcffABJktpsPtLW85KdnY0ZM2bg7rvvxpQpU/xKP2Mt4YHSzTffjLPOOgunn3663/X79u1DWVmZ3+RHo9GICRMmYPXq1W1uz+l0or6+3u+LqUuj0aB3797o3bu3Ly3NGGOMdWSLFi1CYWEhCgsLMW7cOKxbtw6fffaZr/QtJSUFP/zwA3r27IkZM2Zg8ODBuPrqq2G325GRkRFwm08//TQuuOACXHHFFRgzZgx2796NxYsX+5049s7FmDBhgu/k5IQJEyCKouJAyWw2+7r0BTJ16lTfRPmxY8fi+OOPx4svvohevXoBoP//X3zxBZxOJ4477jhce+21vo54bbnhhhswY8YMXHLJJRg3bhyqqqp8DQpi6bzzzsM///lPPPfccxg6dCjefPNNzJkzp1Wp4qOPPoqPP/4YI0aMwPvvv4958+b5AlK9Xo+PPvoI27dvx8iRI/HMM89g9uzZQff70ksvITs7GyeeeCKmT5+OqVOnYsyYMX73eeyxx7B//37069cvYFmmkvFH4u2330ZhYSH69euH888/H1u3bsUnn3zi18AikvdzS1lZWZg/fz5OPfVUDB48GG+88QY++uijNqfQBHterrnmGrhcrjazobEiyIEKTuPk448/xhNPPIF169bBZDJh4sSJGDVqFF5++WWsXr0aJ510Eg4fPoxu3Zo64vz1r3/FgQMHsHjx4oDbnDVrll93Fa+6urqwX1jGGGOMqc/hcGDfvn3o06cPTCZToofDOjlBEPDFF1/gvPPOS/RQWAjz5s3DbbfdhiNHjsBgMIS8f7DPmvr6emRmZoYVGyQsHVBSUoLbbrsN//nPf4J+WCqdiHf//fejrq7O91VSUqLamBmRZRlVVVWoqqoKOLGTMcYYY4yxaNlsNmzZsgVPPfUUrr/++rCCJDUlLFDasGEDysvLccwxx0Cn00Gn02HlypV45ZVXoNPpfIuglZWV+T2uvLy81QJpzRmNRmRkZPh9MXVJkoTVq1dj9erVEXVWYYwxxhhjLJRnn30Wo0aNQn5+Pu6///647z9h7cFPO+00bN682e+6q666CoMGDcK9996Lvn37oqCgAEuWLPEtTuVyubBy5Uo888wziRgyY4wxxhjrILgqJvnNmjULs2bNStj+ExYopaenY9iwYX7XpaamokuXLr7rb7/9djz55JPo378/+vfvjyeffBIpKSm49NJLEzFkxhhjjDHGWCeRNAvOBnLPPffAbrfjpptu8i04+9133yE9PT3RQ2OMMcYYY4x1YEkVKK1YscLvZ0EQEp5yY4wxxpi6uOSJMRZLan3G8CI4jDHGGIsLrVYLgOYcM8ZYrNhsNgC0FlY0kiqjxBhjjLGOS6fTISUlBRUVFdDr9bxoOWNMVbIsw2azoby8HFlZWb6TM5HiQIkpJgiCb9XqYGtaMcYYY80JgoDCwkLs27cPBw4cSPRwGGMdVFZWFgoKCqLeDgdKTDGNRoN+/folehiMMcbaIYPBgP79+3P5HWMsJvR6fdSZJC8OlBhjjDEWVxqNBiaTKdHDYIyxoDhQYorJsoy6ujoAQGZmJpffMcYYY4yxDodnUTLFJEnCqlWrsGrVKkiSlOjhMMYYY4wxpjoOlBhjjDHGGGOsBQ6UGGOMMcYYY6wFDpQYY4wxxhhjrAUOlBhjjDHGGGOsBQ6UGGOMMcYYY6wFDpQYY4wxxhhjrAVeR4kpJggCBgwY4LvMGGOMMcZYR8OBElNMo9Fg4MCBiR4GY4wxxhhjMcOld4wxxhhjjDHWAmeUmGKyLMNisQAA0tLSuPyOMcYYY4x1OJxRYopJkoQVK1ZgxYoVkCQp0cNhjDHGGGNMdRwoMcYYY4wxxlgLHCgxxhhjjDHGWAscKDHGGGOMMcZYCxwoMcYYY4wxxlgLHCgxxhhjjDHGWAscKDHGGGOMMcZYC7yOElNMEAT069fPd5kxxhhjjLGOhgMlpphGo8GQIUMSPQzGGGOMMcZihkvvGGOMMcYYY6wFzigxxWRZht1uBwCYzWYuv2OMMcYYYx0OZ5SYYpIkYdmyZVi2bBkkSUr0cBhjjDHGGFMdB0qMMcYYY4wx1gIHSowxxhhjjDHWAgdKjDHGGGOMMdYCB0qMMcYYY4zFmrMh0SNgCnGgxBhjjDHGWKxZygFZTvQomAIcKDHGGGOMMRZLkkQZJZcl0SNhCvA6SkwxQRDQu3dv32XGGGMsIUQ3oNUnehSMhea2ApABpwUwpid6NCxMHCgxxTQaDYYPH57oYTDGGOvMRDdQvQ/oOiDRI2EsNGdjJokzSu0Kl94xxhhjrP2xHKWz9C5rokfCWGjeAMllpTI8ljiSGPZdOVBiEXG5XHC5XIkeBmOMsc5I9AC2KrpsrUjsWBgLRZKaBfQyZ5USTcHzz4ESU0wURSxevBiLFy+GKIYflTPGGGOqsJYDcuNZeXstBU6MJSuXBUCzbnfcJjyxXLaw78qBEmOMMcbaD0kErJXNrpABW2Wbd2cs4VqWh3JGKbHcHCgxxhhjrCOyVgJyi2oGWxWvT8OSV8vAyG1TNE+GqUiWOVBijDHGWAckSVR215LoAhy1cR8OYyH5zU9qhsvvEsNtayrbDQMHSowxxhhrH2xVgNTGfCRrVXzHwlg4Ws5P8ruexZ3CLpkcKDHGGGMs+ckytQRvi6sBcDviNx7GwtHWgbmTA6WEUBigcqDEGGOMseRnqwYkd/D7cKtwlmzaOjD32LlbYyIo6HgHALoYDYN1YIIgoKioyHeZMcYYa8VtBzR6QKvCoUaobJKXvRrI6AZotNHvk7FotTU/ycvVAJiz4zeezs7jDH2ypQUOlJhiGo0Go0aNSvQwGGOMJTNbNQVLXfoB0Z5Us9cAojP0/WSJ9pvWNbr9RcNWDaTkJG7/LHm0NT/Jy8mBUlwpnJ8EcOkdY4wxxmLB2UBnzOtKot9WONkkr0SuqSRJQO1BwFGfuDGw5BFqPgzPU4qvCBpocKDEIiKKIkSR1wBgjDEWgOimORgAdaqzRDF3yF4LeBQ0afA4Etd62W0FIFNwyOvksFAZDNEJeFzxGQvjjBKLD1EUsXDhQixcuJCDJcYYY621DFTqD0eeZVGSTfJKVFMH70Rx0QXUHUrMGFhyCDU/yYvbhMeHJCo74dKIAyXGGGOsvXPb6cAsWbTK6MhAzX7l7bsd9bRApFKO+sScqW9+0GuvpmwY65xCzU/y4oVn4yPCgJQDJcYYY6y9czYAzrpEj6JJoIM/WQSq9yorSYskm0Q7S8xcpZZBXV0Jt4DurMI9MOeMUnxEUHYHcKDEGGOMtX8uC+BIkkDJbW+7Ba/opMySHM6Zdkt0B5G2qvD2oxa3A5BaBEWSB6g7GL8xsOQRbqMG0UVtq1lscaDEGGOMdVIuK5WbxTMwaEuoUiJnfXjzdyLOJjWSPNRWPF7aKhF01FHLcDXIMnXVqz3ImapkJknKSka5+11syXJkJbzgQIkxxhhr37yZDFlMjvkO4YzBVglYg5TGuWwUUEUr2D7UFiz7VXco+jlTkkSli7Yq+qrYBlirotsmi41w5yf57p8Ef7cdmdtGa6xFgAMlxhhjrD1rXlLiqE3YMAA0dvoK8+x43aG2gypLmTrjcVubOtHFWrD9yGJ060mJHqBqt3/w6C3rq9xF5Y4seSgtGeWMUmxFWHYHcKDEIiAIAgoLC1FYWAgh2tXWGWOMRaf52ehEz1NyWRScuW3shNdyfobbru7vEY9W4ZLYtG5UW5z1kWW4PC6galfjGk0BuCxAxQ4KPHntpuSgNPCR3BzsxlIUcx11Kg6DdRIajQbHHntsoofBGGMM8D9bKnnoIM2YlpixKC39kzxUTpY7ANBo6bpo5ya15KgFxO6ANoaHPOGesa4/DBjTAZ0xvPu77UDVnrabY/jIFBDaa4HM7oA5O7ztM/UpnZ/k5bQAerP642GcUWKMMcY6JY+LumY1l8jyu0jmSHkclFkCKLuk9tpDskRzemIp3AMxWQJqDoTZ9a+ByupCBknNSG56Lqv2KO+kJol0sG4NMX+MBad0fpLvcTxPKSY8ztbdKBXgjBJjjDHWXgUqKXHUAZk94j8W0R26/Kwtznqg7jDN5YnkIDMUWyWQnq/+dr2UZBDcVsBSHnw89hoKqCJ9Lpz1QPk2IC2fvjTNzotLEr1ObgcFqR5H2y3dU3Mj239nFmmZF89Tio0oskkAB0osAqIoYuHChQCAadOmQavVJnhEjDHWSQU6CBBd1FjAkBLfsUTbcc9aDiBG815FFwWQpszYbF/pwVhDKWDKCFxqZakA6sNonx6STE0x7DX0e3scdHZdDDPTVHeISgSN6SqMpR3wuABBE32JZqQBjywm5u+2o4tyQV8uvWOMMcbaq7YOAhJRfqdGO+9YZJO8YlVO5rY3ZsKUkAOX4NUfUSlIakZ0UhDqrA8/SAIAyED1vs6xGKrHSQ0z6g9Ht51I5yd5RXlQzwKIMqPEgRJjjDHWHokeyhIEkojud8leOuSsj81Bf6QHYh47ZZYACphqDqjfyCJaskjNNjpyNz23g+aCiS7AXh1dZjTS+UleybAOWkcS7DMyTBwoMcYYY+1RsLPPHgcdAMZLW3Nckk0sskrRnLG2lAOOegpG7NXqjUlN3mYb4TSgiKVYrIflslEmqfl7t+5Q5L9rtBkhlyXxz3NH0lZLfQU4UGKMMcbao1AH6PEsv2svZ8LtNepvM6rSHhmo3qNS2WIMOespgEgUlw2o3EmlgGplt1xWWsS3ZUc0jwNoiHDB42izqrIUdakYa0aF55IDJcYYY6w9ChkoxbH8rr0ESpJb3bGKHoXzftoxWyU1mkiEukMAZAr+K7ZH/xo6GyhIamtumeWo8oxstPOTvHiekno4UGKMMcY6oXAOytw26uQVj7G0p4M7Nddpak+/txrqD1OpYDxZKvxLqEQXBTn1RyIrU/OWOspSkDvJQF2Jsu1GOz/JK9nn+rUXssyBEksMQRCQl5eHvLw8CEKMWrkyxhhrW7gHZfEov3NZQhx0JhlHnXrzQNTIILQrMs1Xitf8N9Hd1PCiJctRKsdTMhZ7bRhBUiOXBbAqWKhYraDZbeV5SmpwWaFG4MqBElNMo9Fg3LhxGDduHDQafgsxxljchXumNB7ld+2l7M5Lcqt3UNsZ55N4O+GJntD3jVZdSfDW624bULkjvCYdtmoK8pQcPNcfDv/3VCsTJLezDG2yUukkBh/lMsYYY+1NuAfoLgudlY+l9hYoAeqU38lyJ8woNRKdse+EZ68NL9CXJQqoqva0HdRYq4DaA1CcYZDF8Na1Umt+kheX30VPpWCTAyXGGGOsPZFlZW1vY5lVEt20HlB7o0b5ndvWvkoO1eZqUD6PJ1ySpHzxV2c9ULGt9fvdUgHUHYx8LPaa0POy1Jqf5Lc9FhWVsr0cKDHFRFHEwoULsXDhQohiB16EjjHGkpHSA/RYBkrtMZsEqFN+F4t1fdobWxWtBaW2hiPUtEEpyUNlgbUlFGw1lIWXEQql7hBtry1qBzYua/D9seDcjtZt3yPEgRKLiCiKHCQxxlgiKC3LcTaot/ZMq20n+fo/wURbfsdn/Un9EXWDcZct+oWBbZVA+Za2G0EoJToBS5C1lVQvlZMpY8cio+LcQQ6UGGOMsfZE8QG6HLusUnvNKAHUETCa8rvOOj+plcZOeGrN+6orgSplbCplFHws5YA7QJmp2vOTvHieUuSUlCaHwIESY4wx1p5EcrY0Fm3CXTb1D0bjSfJEnhUS3ZGVhnVUsgTU7KNSt2hYK5I4AJWppK8ltecn+W1XAUmi+VRcEqpqRkmn2pYYY4wxFltue/B2yW1xNtCBlJpLOrTnbJKXvRYwpit/HJfdBdZQSu/RrF7K32sel3qlcrHitlJZYGpu03Wxei+4bVQyq9EGv5+zgQIkey19NhjSgNz+sRlTeyB6AI9663xxRokxxhhrLyI9UypL6s8n6giBUqTld51x/aRwOWqBql0U+ChRV9I+ugjWH/FvuR/LErm2/sbcdhrH0S1A1W5qquE9geKydIy/zUipHLgmNFB6/fXXMWLECGRkZCAjIwMnnHAC/ve///lul2UZs2bNQrdu3WA2mzFx4kRs2bIlgSNmjDHGEiiaAyA1y++kDrIoZqTld1zeFJx3Idhwgwh7TftpDCKL1AUPoIxPLEsFm783RTfNk6rYAVRsByxH2y7/bDgauzElO5VPYiQ0UOrRoweefvpprF+/HuvXr8epp56Kc8891xcMPfvss3jxxRfx6quvYt26dSgoKMDkyZPR0NCJI+Uk0aVLF3Tp0iXRw2CMsc4lmoMAR716C4S6GhCTeRmJoLQJQawm73c0koeyHdaqEPcTgTqFayYlmqOWGqTEan6Sl7MBsFXTYrpH/6C1pcJ577kaOm8zCJX/NgVZjuWyysrl5OTgueeew9VXX41u3brh9ttvx7333gsAcDqdyM/PxzPPPIPrr78+rO3V19cjMzMTdXV1yMjIiOXQGWOMsdjxOIHyrdFtI6cfYFLhf2HdIZp43xFodED+MEAQwru/00KlZSx8qV2BjO6Bn+PaEmrn3d5oDYAxI3nHbswAuvRL9CjiS5KAsk0IFbzWN1iQOWh8WLFB0sxREkURH3/8MaxWK0444QTs27cPZWVlmDJliu8+RqMREyZMwOrVq9vcjtPpRH19vd8XY4wx1u6pUVKiVvldR5oDobT8jrNJylkrKCsituiS6LImb6ARiuhK7rE769vvXLpIczhuG9TO8CU8UNq8eTPS0tJgNBpxww034IsvvsCQIUNQVkYtJvPz8/3un5+f77stkKeeegqZmZm+r6KiopiOnzHGGIsLNeYEqbGekuhWtatUUlBSftcR5mYlgqsBqNzZtBaR3Ea7baaeaNu1J4LTQplzW7Xyx8YgMEx4oDRw4EBs3LgRa9aswY033ogrr7wSW7c2lRYILdK0siy3uq65+++/H3V1db6vkhL+I1SbKIpYvHgxFi9eDFGM0WrvjDHG/Kkx50DyRL+djpRN8lLS/Y4bOUROdAKVuyhgt1YAngALuDL1OOvb1/vV2QBU76FsXe0BoHpv6yxkMDE4iZHwdZQMBgOKi4sBAMceeyzWrVuHf/7zn755SWVlZSgsLPTdv7y8vFWWqTmj0Qij0RjbQTO4XLzQHmOMxY3opoNMNThqAWNa5I9vL93JlPCW34VaU8njBCR38Puw4GSRDoAR5pwwFh1LGZDTV73tiR5a2yncOX3hctQBNfv9W8Q76gDXNiCzCDBnhd5GDMpiE55RakmWZTidTvTp0wcFBQVYsmSJ7zaXy4WVK1fixBNPTOAIGWOMsThT80xptOV3HTGjBIRXftde53wkpaTqJdZxOerUyyrJMgW5VbuVr5MVjL0WqN4XeB0tyQPU7ANqDlCHxLa4HXRflSU0o/TAAw/gzDPPRFFRERoaGvDxxx9jxYoVWLRoEQRBwO23344nn3wS/fv3R//+/fHkk08iJSUFl156aSKHzRhjjMWXmgfooosOnAwpEYzDFpODkaTgqAXkHsHPlHOgxNojtbJKdYcAd+PfQOUOIKsnYMqMbpv2GgqCQgXO9mo6YZTVM3DmN0Z/mwkNlI4ePYorrrgCpaWlyMzMxIgRI7Bo0SJMnjwZAHDPPffAbrfjpptuQk1NDcaNG4fvvvsO6ekhUuOMMcZYR6L2QYCjNrJAqaNmk4Dwyu+44x1rjxx11ERDb458G7Zq/y5/koeyS6l5QEa3yErxbNVA7UGEnV0UXZTNSu0KpHcDNM0K42LUZCWhgdK7774b9HZBEDBr1izMmjUrPgNijDHGko0kqn+A7qijgxulOnKgBFAJUFuBkiQ1dWxjrL1pKANy+kT2WJcNqGujOZq1nE7kZPcCdAp6BNiqqWFDJKwV9FmU1RMwpDaOMTYZpaSbo8QYY4yxZmJxptTjACwV1AFPDLM5gSR1/NbYwbrfua3geTWs3XLU0jwepcTGOUKB5g95ua1AxQ4qowuHtTLyIMnL46AOivVHaL6UWs1uWkh41zvWPmVlZSV6CIwx1jnEal5M/aGmy4IG0BoBnQHQmZoue78DtA5ORw8UgpXf8fwk1t5ZyoDs3soeU3uASt5CkUXqWue0AJlB5vpZKvw/e6IiA5ajgK1Kpe21xoESU0yr1eKUU05J9DAYY6xziMcBuizRmjYeO4AWXfG8QVSwM8odib2GAyXWMdlrgLQCQG8K7/71pcqXA7BVUqlwdu/WpXgNR4GGI8q2F44YNpjh0jvGGGMsWUlS4g/QvUFUjEpbko6jLnD5XaJfB8bUYDka3v0cdZSBioTbBlRsp3lIXg1lsQmSYowzSowxxliyctvQ4cvdkk2g8ju3g0qLGGvv7DVAekHwxgseZ2PL7ijIEpXtuSyARhd+gJZkOKPEFBNFEUuXLsXSpUshivyPgzHGYqajN09IVi0npXM2iXUYMmV32iJJjYu/qnR8Z6tqt0ESwIESi5Ddbofdzm1SGWMspvgAPTFalt+5+XVgHYi9hjrFBVJ3sHGuYgfmCb+MmAMlxhhjLBnJMgdKiSJ5/NeM4teBdShy4PlHlorwW3y3V7KMtIU3hX13DpQYY4yxZOS287yYRHLU0ndJpDVbwlG9F9j0SUy7cDGmClu1f1bJaQHqDyduPPFSdwgaa/ilgNzMgTHGGEtGnMVILG/5nZJ5Yj/9Eyj9HdDogWEzYjc2xqLWuAZRVhEtOl2zH52icczhDYruzhklxhhjLBm5GkLfh8WOt/zOZQvv/rIMVO6iyzsXxW5cjKnFVtXY4W4/ILkTPZr4OLxe0d05UGKMMcaSEWeUEs9RG/7rYClrbOcOoHInULUnZsNiTB2NwX1n6a4peYDDvyl6CAdKLCLp6elITw+wcjljjLHouR08zyUZOOrC73hXtdf/552L1R8PY2rrLJkkAKjYAbitkA1pYT+EAyWmmFarxcSJEzFx4kRotdpED4cxxjoWWQbs1aHvx2JP8tDCmeGobswgpeTS991LONhlLJk0zk/y5I8M+yEcKDHGGGPJQJKoPW/51na9QGOnVb2Pvg89HzBnU5vlg78kdkyMsSaHaH6Sp2BU2A/hQIkxxhhLJEkEGo4C5VuA+kOA2MZCkCy5eTNKuf2B/pPpMjd1YCw5uG3A0S0AAE/B6LAfxu3BmWKiKGLVqlUAgFNOOYXL7xhjLBKiG7BWANZKXi+pvfM4gbpDdLlLPyA1F9j0KXBgNWCvBcxZiRwdY6z0d/qcTe8GOa0g7IdxRolFpKGhAQ0N3LqWMcYU8ziB2pKmEjsOktq/2gM0l8mUCZhzgJy+QO4Aem33LEv06BhjjWV36H6MoodxoMQYY4zFg9tO65WUbwNsleE3CWDJz9sKPKcvIAh0ecAZ9H0Hl98xlnDehWZ7cKDEGGOMJZfag0DFdprgD1n97csSdctjiVHd2Bo8p2/TdcWnARo9ULULqNqdmHExxqi8uWY/AAHoNkbRQzlQYowxxmLJ4wJsVbHbvssCfHgxsOje2O2DBecLlPo1XWfKBHqdSJc5q8RY4nizSV0HAqYMRQ/lQIkxxhiLpViviXTkdzpjWrI2tgEZa1ugjBIADGwsv9u9hJp3MMbiL8L5SQAHSowxxlhs2WIcKFVsa7pc9kds98Vas1U3llQKQE5v/9t6jKU1lRx1QAmvqcRY3Mlys/lJxyp+OAdKLCJmsxlmsznRw2CMseTmsgKiM7b7KG8eKG2K7b5Ya95sUmZ3QGfyv02jA/pPocu8phJj8Vezj7L6WiOQP1Txw3kdJaaYVqvF6aefnuhhMMZY8ot1NkmWqEmEV9nm2O6PtdZW2Z3XgKnApk+AAz/zmkqMxduhxmxS4QhAa1D8cM4oMcYY69xi1S1OlhtLsmKoroSyVkLjwt9VuwGXLbb7ZP4CNXJoLqcvTSKXRWD30viNizEWVdkdwIESY4yxzkp0A3WHgKNbACkGaxo5amO/mGx5YzYpfwiQXkAZpvKtsd0n8xcqowQAA86k71x+x1j8iG6gdCNd7s6BEosTURSxatUqrFq1CqLIK8ozxtoZb4BUvhWwVgCSOzbd4mKdTQKa5id1HQzkD6fLXH4XP5KH5kAAQJcggVK/UxvXVNoNVO6Kz9gY6+yObgE8DmqoktMnok1woMQiUltbi9ra2kQPgzHGwid6gLrDTQGS3CyLZC1XtwRP9ACOevW21xZvx7u8wVSDD3BDh3iqP0yBt84EpBe2fT9TBtD7JLrMWSXG4sNbdtf9GECILOThQIkxxljH5guQtjQGRAHK7EQXlcqpxV4DIEZzn7w8TqBqD13OGwQUNGaUjm6lTEdHt3UBMO8iYPeyxI2hylt21yf0gdgA75pKS3lNJcbi4XDk6yd5caDEGGOsYxI9QP2R4AFSc5Zy9fYd60VmASrjkjxUVpJWAGT1BIwZ1I68cmfs959IlnLg539TZvD72cCO/yVmHNWNgWpbjRya63EsYM5pXFNpTWzHxVhn52wAKnbQZQ6UGGOMsUbNAyTL0dABkpfbRv9co+V20LZizTc/aRAgCJTRKOgk85TWvkUBoT4VgAysfAbY9nX8x1HdOD8pWCMHr+ZrKu3g8jvGYurIRvrsz+oJpOVFvBkOlBhjjEUu2VpRWytpDpKSAKk5NbJK8cgmAU3rJ+UNbrrOGyiVduB5SuXbGttsC8DZLwBDZ9D1q14AtnwR37H4MkphBEoAMLCx/O7gmvg0+2Css/KV3UXW7c6LAyXGGGORcdTTgWKs1iFSwu2gbmJ1JdG15HbW07aiEa8DYG8b8ECBUtnm5Hhd1CbLVHIHAAOmUDbtxL8BIy6h6376J7Dp0/iMxWUFGsrocriBUnZv6lDIayp1PhU7gI8uTVyZaGdzqDFQinD9JC8OlFhEDAYDDAblKxwzxjoISQRqD9IcmUSeGZdlOlit2A64LOps0xpFVsnZQI0hYs1RR+WFAAULXrkDAK2RAr66g7EfR7ztXQ4c/YO6zI29jq4TBGDcDcCoy+nnNa8BG+fFfizesrvUXOpqFy5vVmnHoo4ZzLLANswBGo4AG+ZGlu1m4WsopY6UggYoHBnVpjhQYopptVpMnToVU6dOhVarTfRwGGOJUHeI1h8CqNwtEZwWCpAaSqFqhzlbdeRdyWxxKrvzLjSbWQQY05uu1+qbMkylHWyekscJ/PImXR51KQUoXoIAjL0GOOYq+nnt28CG92MbiChp5NCcd02l6j3UkIN1fPWHgYO/0GXLUaD098SOp6PztgXPGwIYUqPaFAdKjDHGlHHU+c/DcVupDCleJBGoLQGqdtFigqqTqZuaUpKkbovxYJqvn9RSR23osPkzOshM7QqMuLj17YIAHHNlU6Zpwxxg/buxC5aqva3Bwyy78zKmA71Ppsu8plLnsOUr+J3M2bk4YUPpFA41BkpRlt0BHCgxxhhTQvRQkNJSvLJK9lrKItlivD9rJQU+Sjhq41dS07zjXUvJsPCs2wYsexz4Y74627NVNZXTjbueSu/aMvoy4Pib6PJv/wF+eSM2wVKkgRLQtKbSriW8plJH57YDOxbS5VGX0fd9K+l6pj5Z8l9oNkocKDHFRFHE6tWrsXr1aohiFJOmGWPtT32zkrvm7DUURMWK6KY5ITX74jMHSBbp4FyJeJXdyXJToJQ3pPXteUOoNr+hNLLMmBq2fwvsWQasfkWdYGndu3RgmTcY6Hda6PuPuBg48Va6vOkT4OdX1Q2WZLlpsdkuCkvvADrTnZJLc8kO8ppKHdquJTR/MqMbcOzVQHo3ei/vX5XokXVMVbvp70qfEjjjrhAHSiwiVVVVqKpSeBDBGGvf7LVBGjfIygOLcFmrKDCIV1mbb7/l4R9ce1yAS4U1mMLRcIQOBDR6oEuAbIYhtWneTCLK72QZ2Lqg6efV/wJ2L4t8e5W7mjqFnXALldiFY9gM4JS/0+U/Pgd+fEm9jJ/lKJWcanQ0T0wpjRboP5kuc/ldxyXLwJbGEwVDzqfXfUDjWlpcfhcb3m533UbT32eUOFBijDEWmuih1tvB2CrVL3FyNHZvi6bld6REV/jBWTw7/3kbOeQWA9o2uo/6yu8SECgd+Y3eK3ozMOgsADKw4ing0Drl2/K1A5cpk5Q/VNnjB08HJtwLQAC2LQB+eJ7muEXLW3aX1ZMaaETCW3538Of4ZSNZfJVuBGr2U6mot9uhd9Hhw7+qs24b8+dbPyn6sjuAAyXGGGPhqCuhVuDBKAkswiHL1C0qkcI9kInXIrNA0/pJXYOUlfgaOiRgntLWL+l7/6mU0el3Kr13vnuoaezhOvAjHWxqDcC4v0Y2noFnApMeoHLEHQuBde9Etp3mopmf5JXdi0qDZAlY9hit/1T2B3X3Yx2Dt+y0/5Sm7pQZ3YCCEQBkYPeShA2tQ/I4m04O9eBAiTHGWDzYa8IPgKwqlt9ZK2PU1U4Bt43WRgrGZYvvOCsaM0rB6u+9gVLVXvXWlwqHtRLY/yNdHnIOBScT7wd6jKXn6H/3ATUHwtuW6ALWvE6XR1wMpOVHPq7+k4EJ99Dl7d9E30ChKsLW4C0NPoe+l26k9Z8W3ALMmQZ8cT0tnrvrO2rFH8/1luoPA79/nNj10TqChjLgwE90eej5/rcNmErfdy7mtbTUVLaZ/rZTuwKZPVXZJAdKjDHG2ia66UAtXK4Gdbo5SSJgKYt+O2oIlVWKZzZJdAOVO+lysEAppQuduYYMlG2Jy9AAUBAiSxSoebMtWj0w+VHKgDnrgYV3h5ep2/IFLaprzqF1k6JVPJm25Wxo6ooVqZrGxWajySgBVH53zivAcdcBvU4CzNlUZlqxg37/5U8Cn1wOfHAuBZm/fkBzMGLRMU0SqfHFZ1dTp8BF98encUpHtfUr+lvoNgbI6eN/W9+JlCWtPUivNVOHd35S92PDn8sYguJZTk6nE2vXrsX+/fths9nQtWtXjB49Gn369An9YMYYY+1LOCV3LVkrgawIJrg311CqfL+x4qwH3A5AH6AltSzH98x79V4KlozpQEb34PctGEGBRtlmoOe42I9N8lCgBABDzvO/TZ8CnPkU8NXf6D218G4KEEyZgbdlr6WgAKAgQp8S/fg0WjpA3TIf2Lsc6Hl8ZNvxOOkAFwjcTEMJQaDXqaBxTpksU6OI8q3A0a20XlbFTnoPlqyhLwAwZgBjrgCGnNv2PDUlqvcBK59tWp8LAmUu17wBnHRr9NvvbDxO6vwIAMPOb327IRXofQp1hty5CMgL0OafKec9AaJS2R2gIFBavXo1/vWvf+HLL7+Ey+VCVlYWzGYzqqur4XQ60bdvX/z1r3/FDTfcgPT09NAbZO2aVqtN9BAYY7Fmq6bFZZWyV1M2QxPh54TbEb91mcJlLaeJ+y056uIb0DVfPynUGdOC4XQQdjRODR0O/EyvmykL6HNK69tNWcBZzwNf3QzUHgAW3Qec9ULgIGjDXFrEuEv/pjIlNfSbSIHS/h8pWxJJkFF7kDIFxnRq8a0mQQDSC+ir36l0neiiUr/ybRRAlf5Obd9//jd18zv2aqD4dCpzVEp0Axs/BH77P3of61OB428EUnKAxQ/Qc1U4ggJMFr4931Nwm5YP9Dwx8H0GTKVAac/3wAk3R94UhBF7LS1CDqjWyAEIs/Tu3HPPxYUXXoju3btj8eLFaGhoQFVVFQ4dOgSbzYZdu3bhH//4B5YtW4YBAwZgyRKenNaRabVaTJs2DdOmTeOAibGOSmnJXXOyFF0Xr/rD8FvFPhnYqgPPa4n3PI6KIOsnteTNUpRvjU8JlbeJw6Cz2g5A0vKBac9TRqR8G7DkkdbPa/U+6lAH0AFkJAFAW/KHAam5FIR5y3SUat7IQaXynqC0BiqzHDYDOPUfwJ8/AsbfRUFaQxmV582/Dij5Rdl8l4odwBc3ABvmUJDU8wTgojnA4LOBXic2lTuufDbyz4JEKd9KHQ7Xvk2vczznEMpyUxOHIee1fcKo+zFUIstraanDm03q0o9KWFUSVkZpypQp+Oyzz2AwBP7g69u3L/r27Ysrr7wSW7ZswZEjR1QbIGOMdUiyTI0CDKmJHklgtVG25LZWAGldlT/OUU8HDklHpt8po1vTVZIYWcYtGr6FZsMo1cnsQVkcRy2VbxUMi924aksaD1QEOtAOJrs3cMZTwLd/p5bhK54GTn2wKSBa8xoF273HA91GqTtOQQP0mQj88V86k9+rjbP9wVSr1MghUhodMOhsyiL98TllhKr2AP+7l9aOOe764O8Pj5Mydps+acyMZVB5Xb/T/AO/Y68Gjm6hDNbSR4BzXwN0xpj/elEp30a/W8kvTddtnEdrjuUPoeen2xgKPGOVwTn6B2U2tAZg0LS276fR0ry5TR8DuxYHzsKy8HkDJRWzSUCYGaWbb765zSCppaFDh2Ly5MlRDYoxxjq82oON802SZB5Oc9aq6IMV0ak8iJBlmlOTrKyVgNRswVJ7DeKa+XJZmubGBGsN7iUI8WsT7s0A9RwHpBeGvn/+UGrwIGip/Gj1q/T6H/yFgieNHjj++tiMtd8k+n7gp8hacVep0BpcDToTMOoy4E8fAsMvpufsyG/AlzcASx8NnAUq2wR8fi3w+0cUJPU7Fbj4/cbSvRbZMY0OOPUhOjtftQdY/Up8fq9IlG+jZhdf3khBkqCh9vQDplIHNMlNAd+GucDXtwLvT6d5chs/pHXJ1Cyf9WaTik9vew6el3fx2YNr4r+gdkciy80CpWNV3XRUS9ZaLBZIzf9pAMjIyIhqQCz5SZKEdeto4cCxY8dCo+HmiYwp0nC0qVNa3cHEH3A153Gpt3aRtTL0gUJztirAE4NuXmqRRRqjN1MW70VCvQvNphcC5qzwHlMwAti/KrYLz3ocNBcKaN3EIZiiccCk+4HvZ9NcGGM6NVkAgGEXhG5WEam8IVQCaDkKlKxVfibfW3oXbSMHtZgygRNuotK89XOopfje5cC+H2jB3TFX0OK/a98GtnwJQKaSr5PvBHqfFHzbqblU7vftXdScoGCEunPGolW+Hfh1blPpmqChNYvGXNH0/vGux3bkN1rk9chvFJQcWte0CLIhFSgYCfQ/vWluWCSslfS8A61bggeS0xfIHUCdLHd/T68hU67uEP09a/RNi22rRHGgtG/fPtxyyy1YsWIFHI6mmk9ZliEIAkQxAauns7iSZRnl5eW+y4wxBew1QEOzrImjjg64U3ISNyYvWY6+5K45Zz2dsQ+nXEcSqdNdsrOW08Gj6ALc1vjuO5z1k1ryZpSO/kEZBDXn+3jtWU4tt9MLaL0kJYpPp7+B1f8Cfn2frjNlAWMuV32YPoIA9J1AC7zuXa4sULLXNJ7kEKiEMJmkF1DgOeJiCopK1tC8sZ2LKAi1VtD9Bk6jhg3eBVBD6X4McMxMmsv040t0YN+y3XW8VWwHNrwPHPyZfhY0tFbW6Cuo5LQ5QaDrMntQ4CjL1N7dGziVbqQ5awdX01fDUWDUnyMb17YF9PlZMBzI7R/eYwZMpUBp12IOlCLlzSYVDKNMq4oUB0qXXXYZAOC9995Dfn4+hHhMZGSMsY7AZWsqnWqu7hBgSAN0KrT5jZQk0cGDK8TiqkpZK4HMMDIDDWXJ0w48GNFFZ6PdCVgI19fxTkGglFtMBw7OBqBmf2yyl1u/ou+Dp0fW6XDYBRSA/PYf+vnYq+jvIZb6nkqB0oHVlBEL9+DKm03KKFSnZXksdOkHnPk0BQK/vEUNQDwOCqROuQvoEUFp0ujLKSt5eD2wdBZw/uuJ+f0rdjQGSKvpZ0FD83zGBAiQ2iII9HeQ05fee5JIc4p2LaE5X2vfpPfxiIuVjU10Adu+pstDFQQ8/U4Dfn6Nfrea/ckXgLcHh5utn6QyxYHSpk2bsGHDBgwcOFD1wTDGWIflcdFBliy1vk0WKYDKLY7/uAA6UKiOQZAEUKlaeiEQrETX42w6290eWMrpOYsnWaZOXoCyjJJGR5PYD/9KB7pqB0oV2+lLo6dMRaSOvYYmv9trqWterHUdSO/LhlIq2wq3/bWv412CGjko0W00cN5rwIEfgbrDwJBzIg9uNFpquPH5ddTafdWLwKQH49P1DwDqSynr6BcgnQ6M+Uv4AVJbNFpqt991EGXZNsylhiKCBhh+Yfjb2buCAv7UXGVZSnMWrel14Cdg52JgXIzm5nVUkgc4/BtdVnH9JC/FOfixY8eipKRE9YEwxliH5c3WSAHaS3u5GgBLAoIF0QNU7Y5NkARQEBiqhXYytgMPxm2jZhXxZC2n51HQhl/S4+VtEx6LeUpbG5s49J0QXUteQaCD3pNupeAu1gShKTjaszz8xyVLI4dwCQItbDryT9FngMzZwOkPUwCxe2nT4sKxZq8Bvr2TgiRvBumi94FJD0QfJLU05koq3wOAn19taswQjj++oO+Dz1H+HvbO+9q1JPKTMLIM/P4xsOThjt0Ywntib/tCYNULwPzrqQzamEHrrqlM8afRO++8gxtuuAGHDx/GsGHDoNf7t1ccMULdSVSMMdbu1e6ng+tQGo7QGU29ujXWbRLdFCTFeo0RawWQ2iXwbc6G+LfYbo+8ZXdd+ilv0ezrfKdyoORsAHYvo8tDzlV32/HQbxJ1fzu4hv4+wwkkatpZoKS2ghHAcX8FfnmDuuB1HaQ8cFfC46SFbxtKgfRuwJlPAVm9Yrc/QaC26LJEbcVXv0IZp1Dv7/JtVOKo0VMJqlI9j6fPflsllUxGUh752/8B69+jy1oDNeHoCKwV1LSjfGtjBntH4P+nA8+MfJHzIBQHShUVFdizZw+uuuoq33WCIHAzB8YYC6TucPiBgCxRWUvugNiXtHicFCTFYyFSj50OqgNNHq9TqcNeR+ebnxTG+kkt5Q2hM/GWo/SVlq/OmHYuosxaTl9ayLW96dKfOqPVHwYO/AwUnxb8/t4z2QAFrJ3ViIuB0k2U4Vn6CDDjrdjMKZMlYPkT9N43ptO8q6ye6u+nJUEAxl5Lr/emj6mBhaAJHgBtacwm9ZsUWWZVa6C5St7mG0oDpT/mNwVJAGX8iidTu/72xmWluV5HtwIVW2mea0s6E5XP5g2hz8S8wUBaXkyGozhQuvrqqzF69Gh89NFH3MyBMcaCsVZRyZQSbhs1NsgIYy2aSLnttC5KsFJAtVkrWwdK1iRvB55MIul456U3U/BdsZ2ySsUqBEqy3FR2N+Tc+M1VUZMgAH0nARv/Q/NLQgVK9YfpxILOFN5aUR2VoAEm3gfMv47WPVv5LHD6o+q/B355k1pta/TAlNnxCZK8BIHmCskSsPlTKvEStIEXkLXXNJVvKmni0NKAKRQo7VtFjX8MYZZK7vyuaY2rMVdSoPHHf4EfXwQumpO8TUcCkTy0cPLRP5quEzRAdh/67MsbRMFRVq+YZI8CURwoHThwAAsWLEBxcYImHbOE02q1mD49gtQyY52JswGoi3A+p+UorY0S7j9KJVxWCpLUagEeLkcdNbTwdvaTRP826axtkofKTYDIAiWASqYqtlMmoPj06Md05Dd6f+vNdOa6verXGCiVrKG/DUNq2/f1NnLI7h23g7SkZcoATp8FLPgbBTN/fK6s8UEoW78CNn1ClyfcAxSOVG/b4RIEaqMui/T7/fAcNaUZcIb//bZ9TSedvAfykeo6GMgsor+rfSuplCyU/T8CK5+my8MuoDbuHjutnWY5Cqx7DzjxlsjHFG/r51KQpE+lToZ5g6m0M4HBnuJmDqeeeip+//33WIyFMcY6Bo+T2rxG3KBAphK8Fgt6R83ZQOV28Q6SAAAydcDzshxtH+3Ak0HNAZpHpk+J/Ky62vOUtn5J3/tPiU1AHy85fengVHRTq/Bgqjv5/KSW8gYDx99El9e8Dhzdos52D/4C/PRPunzs1bQ+UqIIAnDCLY0LKcvAimdoQV8vydOUWY0mm+TdlzcI27k49P2P/AYse5SyXgOmAifcTNvQpwCn3En32TK/qWw32R1aT/PCAGDC3dSApHBkwjNiijNK06dPxx133IHNmzdj+PDhrZo5nHPOOaoNjjHG2h1JpAOqaIMAj4MyLmp1dbLXRhm8qcBWSWu5iC5qsc3C03x+UqQLxhY0ziGq2Qc46ikjEClrJZ3JBtpnE4fmBAHodyotdrtnefCD8qo99J0DpSZDzwfKNlHp4rd/p0YPQ8+L/H1atRtYNqvx4P+Mpg50iSQIwEm30Zi2LQBWPN3UnnzfKvpcM2dT58do9Z8MrHuHFsFtKG27xLN8GzW5EN1A75OB8Xf7P+dF42h8u5dSJmzGW/HpJhkpWzWw/EkAMjBoevjt+uNA8bN2ww03AAAee+yxVrdxM4fOQZIk/PrrrwCAMWPGQBNsfRTGOhNZpsneanWRs1ZQCV6gJghK2KobF7pNcAtuyUP1/I7axI+lPaloDJQiLbsD6EDOW9Zz9A+g14mRb2v7N3TQWDC8YwQNfSdSoHRoXdtNRwBu5BCIINBBur0GKP2d5srsXQ6MvwfIKlK2LUs5sOg+mkPZbQxwyt+TZ+6bIAAn307v++3f0EG9oGlq4jB4OjVkiFZaHtB9NK17tmsJtcxvqXofzePxPk+nPhQ4CDrhFqBkLZ24+/0TYPRl0Y8vFmQJWPEUYK+muUhJViqo+AhXkqQ2vzhI6hxkWUZpaSlKS0shy3ywwzool42yMI56OnhyWek6t51K6zwuWoNIEilAAuggVO31iGoPRre4qaWCyviSJTCpP8LtwJXyZpSimf8AqFN+J3mAbY3r57T3bJJXTh+adyS5adHPQFy2pjl1OX2Cb88YRbauPTKkAme/BJx0OzW6KNsMfH4NrekTbmbdZQMW30/ZyqxewORHAa0+9OPiSdBQSdvAaXRw//1s+l0FLa2dpJb+jWsq7Vzc9L/Fq/4IsPAuwFlPc5qmzG57uQBzFpXjAcCvc4G6Q+qNUU2/f0InKbRG4LSHlS9/EGOKA6Vgi82uWbMmqsEwxljCiR4KTip3UJlS9R4qB6ncSddVNK7nUL4FOLqZyk5KN1K9ePM5OKqNx6XsH5wsA04Ldc6r3A3UJ9k/x3h22usI3LbGkklQt6doFKqw8OyB1VRqZMoC+oyPbjzJpO8k+r5nReDbaxqzSSld6HcPJqWNNcM6MkFDJXcXzQG6H0ufW7+8AXx1c9PcrrZIHpprU7WHMp9nPh19Fj1WBA0w/i6aEyQ3ziHtMx5IzVVvH31OoYCz/rD/vC9bFfDtXfQ9uzdw5jOh5wf2n9L4eriBH55vHXgl2tEtVGoIACf9LfRJiARQHChNnjwZVVWtDwZ++uknnHHGGQEewRhj7YStmsqcYhHwRMNeTdmttrhsVLZStYcCt6pdVN+udnaLxV/FTjogS+0a/QG4N6NUsZ2yopHY+hV9HzRNnVKjZNFvIn0/tI6yyC2F28hBo2/MKCVJyVi8pRcC054DJtxLmaaKHcD8vwIb3qeD9ZZkGVj9L6DkF8ooTH0y+VuvCxoqLRx0Nq0fpXZJmz6lab7TrsamDo56CpIajtDCu9OeD2+eoSBQFkxrpBN6OxaqO9ZoOBuAZY9Rc6F+pwIDz0r0iAJSHCidcsopmDJlChoamv4B//DDD5g2bRoeeeQRVQfHGGNx4XZQ9qX2QPJ2YqsroWwXQOV/lgo6eCvdRJmu+sNUjuE9y8k6hmjWT2opvRsFW5KnabtK1JYAhzcAEIIvvtkeZfUCcvrRQZu3UUVzvkYOIeYn6YzUQjpYm/GOThCotfVF7wM9T6T324Y5wBfXN7W599r8aWPwLQCn/kOd93k8aLSUWbrya6BLDJbL8Zbf7VlOczoX3UtZzZQuwFkvKMtgZXQDjr2KLq95PfoTgc6G6P9PyjI1mbAcpc+lZJqP1oLiQOmtt95Cnz59cNZZZ8HhcGD58uU466yz8Nhjj+GOO+6IxRgZYyw2ZBmoL6WDxmTPvkgeKv8r20zjrT9Ec30S0uqbxU35VvreVYUDSEGIbp7StsY2yD3HJf9Z/0h4O23tXd76tnAzSt75FclaOhZPqbnA1Ceo2YAxg57DL2+khWQ9TmDvSmDNG3Tf42+kkrP2JlYH991GAal5gMsCfP5XmqdozKBMUiSLkQ+/kBaddlkogxcJjwNY+w7wf+cDH19GazVFWsq3bUHjYsI64PSHk/rEguJASRAEfPTRRzCZTDjttNNwzjnn4KmnnsJtt90Wi/ExxlhsOBvon4+lDEnT6CAU0Zm8GS8WG+UqZpQAWngWoBJNJTwOYOciutxRmji01K9xntLhDY2dGRvJclOg1CVUoGSi74Y01YfXLgkCUHwacPH7NA9MloDfP6JmD8ufACDTGkXDL0r0SJOLoKH5RQBgLaeFnc98JvI5PBpdUwvxvSuA/W00LWnLwV+Az66ixZklD2WCvnsIWHQ/NZhQomoP8POrdPm4v9KyB0ksrEBp06ZNfl/btm3DI488gpKSElx++eUYP3687zbGGEtqoocmx1ftpsCDsWRlq6KDJEEDdB2gzjZ9GaUt4XdTrNwFrHyOTi6k5QM9jlNnLMkmswfQpT8dzO9b1XS9tYLOxAva0Av+eudtGVIjX0uoIzJnA6c/Akx5HDDnUIMa0QUUHU/toJO07CqhBkyl95BWD0x5IvqTJbn9geEX0+WfXqJOrqFYK4Alj1DpX0MpzZU87RFg1OUUfJWsAT67EtgwN7x5j247Ne4Q3fTaD78wql8pHsJaR2nUqFEQBMGvFbT35zfffBNvvfUWZFnmdZQ6Ca1Wi2nTpvkuM9ZuWCvp7BeXq7H2wNsWPLu3eqvT5/SlbbmtlCXJ7R/4fi4bsGcZsO1rKvn0GnUpzc/oqPpNomYoe5c3zcOqbpyflFUUuoGFN6MkCJRVcgZoDNGZ9T4FKBwFrH+PAu9T7kzuhVATKasImP4y/b2qNQ/q2JlU8tZwBFj7Nq0NFYjkAbZ8Cax/l4IbQUNBzTEzaTz9JgEDpgA//ZMysBvmAru+A068jUpz27L6X9RVNiUXmHhf4k4maMJvPR/Wu3Pfvn0Rj4V1TBwgsXanep9/OQ1jyc4bKKlZmqLRAvlDqbtb2Wb/QEmWabL99q+B3cuaFk7W6OgAd/B0oPsY9caSjPpOBNa+BRzZSF0wU3KazU8KY6HZ5mvAGNM5UArEmA6cxNM1wuItlVWLzkTB6cK7qIlG8elAwTD/+5RvA1a9QFUXAH1enHxn64WWs3rSnKm9K4Cf/00nIRfdS58VJ95C2efmdi+lrnuCBjj1QVrnKVEUzCEMK1Dq1atXxGNhjLGEczs4SGLtT4V3odko109qqWB4U6A0bAad2d+9FNj+TVN3NwDILAIGn00duBJ5UBNPGd0oMK3YTpPVh5wLVIXZyEFr9C8h43lKLBn1OJbK+nYuBlY9D8x4izKlzgZa02jrAgAyBRPHXU9LAbSV+REEyi4VjaNFbTf/l/5uDq0DxlxBpX5aPZVarnqBHjP6CqDb6Hj9toGZMsO+a1iB0s8//4wTTjghrA1arVbs378fQ4cODXsQrH2RJMk3H23EiBHQaLgOmyU5a3miR8CYMrIElDe2Us5TebKz9yx16UZgxdPUgtg7X0+rB/pMpACpYETnnDvSdyIFSnu+p0DJW3oXspGD0f9nQwrNa+JSX5Zsjr+JGjTU7Ac2fkjz837+N2Cvodv7TwWOv4HmloXDkELbHHAGleOV/k6lfTsWASfc0lTCVziSAqhEErSKMkphHeH+5S9/weTJk/Hpp5/CYrEEvM/WrVvxwAMPoLi4GL/++mvYA2DtjyzLKCkpQUlJid+8NcaSkuhp+vBnrL2oLaF5RDoTzVFSU95gKqez11AnO9EJZPcBTvwbcNnnVBZTOLJzBklAU5vw0k1AQxm9FkB4ayi1ZOSsEktCpkz6ewdoftH3s+nzILMIOPslYNL94QdJzeX0Bc5+GZj4AD2+roTK8Sp3UnvzSQ8mfk6aKUPRZ1tYo926dSvefPNNPPzww7jsssswYMAAdOvWDSaTCTU1Ndi+fTusVitmzJiBJUuWYNiwYaE3yhhLDEmibkM6Y+c4ELJV8iKsrG2yDBzdAuT0jl2p1MGfac5Pj+OAPieH15jBu35S7gD1Dyx0Rmo9vHcF0GcCZY/yhnSOz4NwpBfQ81G+Ffj1A8oIGVKp41cw2gCBkiGd1jtjLNn0O5UaMJT8QqV3o68ARl4SumFJKIJAjR56nQCsnwNs/ZL+B0+8D0jLU2XoUTFlAe7w7y7IClMCv/76K1atWoX9+/fDbrcjNzcXo0ePxqRJk5CTk6NwtLFXX1+PzMxM1NXVISMjI9HD6RBEUcTChQsBANOmTePGDu1N/RFaAwGgD0Tvl87Y4nL4XWGSlvcgWFLwqcg6lw3vAxvmAD2PB854Wv3te5zAh5c0zZHTmYDeJwPFk4Eex7QdBP34Ek22HnEJLcbJ4mvTZ8Caf9PcDFmiMsRzXgn+mJx+dLa6Obejaa4ZY8nG2QDsWkKd6jK6x2YftQepi6baJcSREDRA/nDUWyxhxwaKT1ONGTMGY8Z08K43jHVUopvWRfD97KKvQARNU+CU2SNwWUmys9dwkMTatm8VBUkAcHANdTcLNWFfqd3LKEgyZVEZVt0hapyweyld1+9UoP9kaiDQPKPj7Xin1kKzTJm+EyhQ8majw3lfBPqM1JuoFTF/DrFkZEynhi6xFGrtsXgypgMK59XzLHzGOpOGsvDL0GSJ2gM769tv6UjzoJCx5qr3AsufoMuGVPq+6TN19yHLwB//pcsj/wRc/H/Aea8DQ2dQkOSoBbbMB768EfjkcporUHeIslDe7nMcKCVGWh6Q32waQchASWj7ZBLPU2IsOZiyFD+EAyXGOguPE7BVRfZYZ4O6Y4kHpwVw2xI9CpaMHLXA4gfpRED3McAZT9H1u5dG/jcSyOENFJDpTMCgsyhjlDcYOOlW4PL/Amc8Q+uYaI1A/WEKlD65HJj/V5oXY84GUpOgpr+z6ndq0+WWa8i0FCzjbgi/wxZjLFYERW3BvRIaKD311FMYO3Ys0tPTkZeXh/POOw87duzwu48sy5g1axa6desGs9mMiRMnYsuWLQkaMWPtWEMpgAi7FLosdHa8PeFsUuxV7ACWPdbUFaw9kDzA0kfp7yG9G3DaIzT/JH8olUdt+VK9fW1uzCYNPLN1O1qNjuYFnPoP4C9fAJMeAHqMpZLX2gN0H26wkFh9J9DrpDVSV8BgggVKnFFiLPGM6bTgtkIJDZRWrlyJm2++GWvWrMGSJUvg8XgwZcoUWK1W332effZZvPjii3j11Vexbt06FBQUYPLkyWhoaIdnuDsIrVaLqVOnYurUqdzIob1w26NrkS1LgMsa+n7JwuNqv+WC7YXlKPC/e2mtmbVvJ3o04fv5NeDIb4DeDEyd3XSGcfjF9H3rV5RpilbtQaBkDQABGHZB8PvqU6gL3bTngMs+A064mVpUK1pvhAMq1aV0Ac5+ETjreVonJphAHe+8vI1yGGOJE0E2CYigmUNzDocDJpMp4scvWrTI7+c5c+YgLy8PGzZswPjx4yHLMl5++WU8+OCDmDGDJpu9//77yM/Px4cffojrr78+muGzKBgM/KHfrtQfiX4bzob2c2bUWoGIs2csNLedSte8ndwO/AhYypOj9Wsw27+lOUEArefRfN5J75OB9ELKNO38DhhyTnT7+uNz+t7rRGqGEq6ULsDwi+hLCUMarbvErfDV5V2cNxRdiGMhY7q6ZZ2MMWUiDJQUZ5QkScLjjz+O7t27Iy0tDXv37gUAPPTQQ3j33XcjGoRXXR2dAfa2Gd+3bx/KysowZcoU332MRiMmTJiA1atXB9yG0+lEfX293xdjnZrTQg0Zot5OO8niShIfkMSSLAMrngaqdtMcmi796eB8+zeJHllwZX9Qy20AOPZqCoya02iB4RfS5c2fRRdwOOqBnYvpsnebsaY3AzpzfPbFWtOFOHkYqzW6GGOhGdIiXvJEcaA0e/ZszJ07F88++6xfVmH48OF45513IhoEQHOR7rzzTpx88sm+BWvLysoAAPn5+X73zc/P993W0lNPPYXMzEzfV1FRUcRjYoFJkoTNmzdj8+bNkCQ+e5n0GkrV2Y7bBkiiOtuKJVsVTYRnsfHb/wH7VtLcjcmPAaMupeu3fU3t55ORpRxY8hDNT+ozHhh9eeD7DTiTOuDVlVC78Eht/4bK97r0AwpHRb4dJfRmakXNEiOcjBJjLDEizCYBEQRKH3zwAd566y1cdtllfvNTRowYge3bt0c8kFtuuQWbNm3CRx991Oo2ocVkVlmWW13ndf/996Ours73VVLSjiYZtxOyLGP//v3Yv38/FK5XzOLNUU+NGFQht4+sEjdxiJ19q4D179Hlk+8ACoYDfU4BzDk0B27/qsSOLxCPE/juHzS+nH60OrzQxr8+QwowaDpd3vRpZPuTPE3lfcMvil8zBn0KfbH4E7Shz1Zr9aGDKcZYbETQFtxLcaB0+PBhFBcXt7pekiS43ZGdTfzb3/6GBQsWYPny5ejRo6mWu6CgAABaZY/Ky8tbZZm8jEYjMjIy/L4Y67TUyiZ5qRZ0xYi9FhCdiR5Fx9R83aFhF1C7a4AyS4Mbg4utXyVmbG2RZeCH54DKnYAxA5gyO3QwMWwGHfiWbqTHKbV3JWCtpLLE5u2lY0nQUDZJz6V3CRHuYtxcfsdY/OlTQpfGBqE4UBo6dChWrWp91vCzzz7D6NGjFW1LlmXccsstmD9/Pr7//nv06ePffrNPnz4oKCjAkiVLfNe5XC6sXLkSJ554otKhM9a52GvUX0co2TNK1spEj6BjctQCix9oXHfoGOD4G/1vH3w2HayX/k4BVbL4/WNaG0nQAJMfBTIKQz8mLQ/oN4kuK12AVpZpfhMADDkvfp3OvHOTeI5SYoQbKHH5HWPxF0XZHRBB17tHHnkEV1xxBQ4fPgxJkjB//nzs2LEDH3zwAb75Rtlk3ptvvhkffvghvvrqK6Snp/syR5mZmTCbzRAEAbfffjuefPJJ9O/fH/3798eTTz6JlJQUXHrppUqHzljnIctAvcrZJIAOlEV3xJMiY8ptB1xJHsi1R5IHWDILaCgDMhrXHdK0+NeR2pWaI+z7gbJKJ9+RkKH6ObgGWPsWXT7xb0A3BSfyhl9EAdae74Hjrgu/m9/RLUDFdvr7iLZrnhLeTJJGQ+VdarQ3Z+EL1hq8OQ6UGIu/KAMlxRml6dOn45NPPsHChQshCAIefvhhbNu2DV9//TUmT56saFuvv/466urqMHHiRBQWFvq+PvnkE9997rnnHtx+++246aabcOyxx+Lw4cP47rvvkJ7OHziMtclWHbsStGTNKlnKEz2Cjmn1q1SGpk8BpjwBmNooZx5yHn3f9V3i19yqPQh8/zgAGRh0dtPYwtV1IFA4kpqCbPki/Md5s0nFk6n0Ll6alxPyPJj4CzejpNHyPDLG4kkXfUlyROsoeRcbjVY4jQAEQcCsWbMwa9asqPfHWNy57ZTdCbVYoZokSf25Sc05G4CUnNhtPxKiJ7oFdVlgW78Ctn4JQABO/QeQ06ft+3YbDWT1pCBl13fA0PPjNUp/tmpa48llBfKHASfdFllDheEXUynhtq9p4ddQB7gNpU3NLOLVEtyr+YGAPqVpfSsWH+EGSkDjelcql0QzxgKLMpsERJBRWrduHX755ZdW1//yyy9Yv3591ANirENx1AP1h+O7T1slIMWwTXMyZpRsleAFZlV2ZCPw0yt0eey1tHBqMILQlLnZ+hWdIIg1SQSq9gBbFwArngI+uQL4zwxq752aR+3LIy0T7XUCLRTrsgA7FoW+/x9f0NpL3Y/xX8g25oQWgRJnlOJOSRaPy+8Yi59EBEo333xzwJbbhw8fxs033xz1gFjy02g0OO2003DaaadBo1H8FupcHHV0oBWvbIckApajMd6HG3An0RwIWeYmDmprKAWWPkKlZ/1ObVorKZQBU+igsWY/leupzVELHFgNrHsH+OZO4P2zgc+vAX58kRZ4rWv835TTF5j6RHSZT0FDc5UAYPN/g68h5rIB27+ly97HxIvO5J8x49Ku+NLoqKQuXIY0AHFqGc9YZ6Y10Lp4UVJcerd161aMGTOm1fWjR4/G1q1box4QS36CICAlhf8ZhyR6AHfjXI36UurjH+s1VSzlNPk+1pwNyXPm2l4T2wxaZ+O2AYv/QUF+7gBgwj3hv28NaUD/yVSutvUrZQ0U2nL4V2DnIqB8K1B3qPXtejOQNxjIGwrkDwHyhqhyFhEAMGAqsO5doOEIcOAnWqw2kJ3/o7/1zCKg6Dh19h2ulvX3Wj0dvMfjc4ApnxOm0dDBW7IvtcBYe6fS/wHFgZLRaMTRo0fRt69/aUFpaSl0uoimPDHWMTnrmy6LTgpi0gOv/6UK0QNY49TQwNUAoGt89hUKLzCrHlkClj8FVO+hZgRTZis/EBxyHgVK+1ZRpi81N/LxlP0BLLyLxuWV1bMpKMofCmT1UnZGXwmdibrX/fYfWoA2UKAkicDmz+ny8AvbXsw2VgJlkPQp/p8/LHYiaQFvSONAibFYS1SgNHnyZNx///346quvkJlJg6itrcUDDzyguOsda58kScL27dsBAIMGDeLyu7Y46vx/thwFUroA2hidULCU+R9QxpLTQiVvsc6QhRxHA0+MVou9Blj/HjUk0OgpSAq3LXZzXfoBBcOBss3A9m+AY2ZGNh5HHbDsUXpPFx0PDDsf6Dq47a57sTL0fFqP6egflNXKG+J/+8HVlHEypgP9p8R3bEDgjk46EwdK8RJJl0FjGsBxEmOxo9GptsCz4iPcF154ASUlJejVqxcmTZqESZMmoU+fPigrK8MLL7ygyqBYcpNlGXv27MGePXvC6lzYKcly66YHskgHVLHgccV3no4sJkeAwtmk6NWXAj++DHx4CWWCAOCUOylbEylvU4dt30RWAiZL1JzBWkHlbKc9DBSNi3+QBNDJjeLT6HKgBWg3/5e+D54edRvaiATaZyLG0VnpIswoxTvzyFhnYspU7USu4lPb3bt3x6ZNmzBv3jz8/vvvMJvNuOqqq/DnP/8Zen0SLkLJWCK4LBRMtGSrpsU51T6QsZQh7l3fnA2qTJSMmMfZOmvHwle1G9j4EbB3eVMmsusgYMxfQne4C6XPeCrds1UC+38E+k5U9vjfP6EFY7UG4PRZ8W2vH8jwi6lZxL6V1OgivZCur9xFLcQFrfK1mtSgNQYuO+SGDvETSUZJEChY4qwfY7Gh1jxVRLiOUmpqKv7617+qNgjGOpw2D+BloO4wkFus3r7cDgrA4s1pAdTqdCt6AI+DzrJqtHTgKWho4nNbOJuknCxTN7rfPwJK1jZd32MsdbYrHKXOWTitHhh0Fs3t2fqVskCpbDOw7m26fOKtVMqXaF36UdvvwxuoDfgJN9H13gVm+06MrEwxWm2dcNEZ6e8nXqW4nZlWwRpKzXGgxFhsCFrAqF71QUSB0s6dO7FixQqUl5dDkvw/iB9++GFVBsZYu+YI8g/Q1UCBlBpnPCQRqD2AhKwh5LLQ4rZqzFGrPwzYAwV7QmPgpKEPP99lTfs/yKg5AFTuoPbbmhg3wpElYP9PwMYPgYptdJ2goQP8kX8Gcvurv8/B02l/R36j3zW7V+jHOGqBZY/ReItPp2ArWQy/mAKl7d8Ax/yFMpp7vm+8Lc4twb3ayhwJAqAzN3XdZLGhNUT++WdMA5JwSTrG2j1juqrzpxX/d3777bdx4403Ijc3FwUFBRCaDUYQBA6UGHM7qMtdMPVH6IxHNH/MkkSLbSZsrpBMwVK080Y8ziDrTMkds83x7mXAymcA0QWUrAMm3R+bOQuiC9i1hJoReNcY0hqAgdOAERcDGd3U36dXWj6V8O3/Edj6JXDSbcHvL0vA8qeb5iWdfGfim4U0V3QckN2b1ojavpBKTyUPkD8MyBuUmDEFK+HVmzhQirVIs0kABbmCNnCJNmMscuYsVTenOFCaPXs2nnjiCdx7772qDoSxDiOcTIfHQc0X0iJssS1J1MI50QdCzoboAyXLUSQkI5YIkgisf5cyLV67l9BBrdqBQfU+YPH9QEMZ/WxIA4aeBwy7gOYPxcOQ8yhQ2rkYOO664HNnfv8YKEmieUktCQK1//7heWrg4D0ZMvzCxI0paKCUAqAqbkPplHRRBEqCQHM823tmnLFkImgAo3rzk4AIut7V1NTgoosSVGbAWHsQrOyuuYZSmpujlCQB1XuTYx2OaMfgcSVmflUiuKzAd/9oCpJG/hk49R8ABOo2t+Z1mkOkhvKtwNe3UZCUkgscfxNw6afA2GvjFyQBQPcxlB1y24BdS9u+X9kmYN07dPmkJJmXFEjxZFo42lpO5bNp+UDvkxMzFo2e5oK1JZImA0yZaAIlQNV5FIwx0AlBlZesUby1iy66CN99952qg2Dti0ajwcSJEzFx4kReQ6klSQw/eJDFxm51CsgyULOvccHXJOC2RRbseXWWbFLdIeDLm4CDP9PB7aQHgHHX0zyc8XfTfTZ/CmyYG/2+Dq0HvrmTzlTnDQEufI/K7BKRoRE0tGArQOV3gQLBlvOSBibRvKSWdEbKynkNuyD288vaEqpzJne+i72oAyV11nlhjDWKwYlAxZ/wxcXFeOihh7BmzRoMHz68VUvwW2+9VbXBseQkCALS09Vqd9bBOOqg6MDfWkln/PVhnP2VZcokJVuphqshsg8n0Q3YOkFp0KH1tHCqs4Fe6ymz/ee0DJpGpZirXwF+fZ8OgEf+KbJ97fsBWPY4ILmB7scCUx5L/AHzgDOAte/Qe/foZqBgRNNtsgQsf4r+DjKLaP2mZJqXFMiQc6n0TqOl1y5RQr2uGg3NoQk1X5JFLtqsnd5MgXZHnIfJWNwJMcnSCrLCFUP79OnT9sYEAXv37o16UGqqr69HZmYm6urqkJHBaW4WYzX7gzQmaIMxI3SpkTeTlIzrBqV0AbJ6Kn9c3aGO3eJbloEt84Gf/00BQd5gCpJSugS+/2/zmtpin3wHHZArsX0hsOp52lefCcCpD9J8n2Tww3PA9m+pw99pzRr+bJwHrH2bxnne68lbcteSpZyyZam5iRtDdp/Qk5ar91HGjsWAABSOjD6w59eIMXUY0sNeekVJbKA4o7Rv3z6lD2EdjCRJ2LVrFwCgf//+XH7nJcuUNVDKWU/zmtpqiiDL1AI8GYMkgNZTUqqjZ5NEF/Djy8COhfTzgKnUrCFYqc7oy6iUceM84MeX6Gz1gKnh7e/3j4Ff3qDLg86ifQVaiDRRhpxHgdK+H+h1T+kClG4C1r1Lt590W/sJkoDErJnUUjiLVuvNfBAeK1qDOtlPYwa/RoypQeVud158hMsUk2UZO3fuxM6dO6EwIdmxuayRl1DUH257In/tQeVZqngSndTiWwlLecddDNNWRXOEdiykrMPxNwET7gtvPsPYa4GhM+jyymeAvSuD31+WKSPjDZJG/hk45a7kCpIAWqcpfyj9fWxfCNhrge+985ImU7tyFj5BG977KZxgikUm2vlJXjxPiTF1qLE2ZQARzUI9dOgQFixYgIMHD8Llcvnd9uKLL6oyMMbanWjmDnkcdIDdspSn9mAbC7EmGacl/AMH0QPYKmM7nkSp3AksfpBKCg2pwGmP0Po74RIE4MRb6P2wYyHw/eP0vPY8vvV9JRH46WXqmAcAx/0VGHWpKr9GTAw5Fzi6Bdi2gLrc+eYl3ZH885KSTbjzzhI9P60jU6uroM5I2SnRFfq+jLHADOnBu4BGQXGgtGzZMpxzzjno06cPduzYgWHDhmH//v2QZRljxoyJxRgZax+iLY1rKKWmCN5sQG1J+ylPc9YDqW3MvWnJ2kGzSbuXAiufowxbZhEw9Ukgq0j5dgQNcMrfAbcd2LscWPIwcOYzQLfRTfcR3cDyJ+l2CHT/wWer9qvERJ8JNF/LWkFfWiMw+VE+mI9EuJkirZ6bBcSKWhklgFoat4cTYowlI60ByO4Vs80rLr27//778fe//x1//PEHTCYTPv/8c5SUlGDChAm8vhLrvDxOygJEQ/I0LQ5ad6h9ZV3CbYkueiiT0JE0lAKLHgC+n01BUtE44PzXIwuSvDRaasbQ80Q607zofsrGAPQ+++5BCpI0OmqOkOxBEkAHloOatf4+6TYgp2/ixtOeKSmp40C0iSEdSO+mzrbUDJRSctTbFmOdiUYH5PSLWTYJiCBQ2rZtG6688koAgE6ng91uR1paGh577DE888wzqg+QsXYh3EVmQ7FWADUH2l83OMkDuGyh72etoPWjOgLRDfz2H+DTmcDB1fSBPeZKyiQZVJh3oNEBpz9Ci7Z6HMD/7gWO/AZ8exdQspZKf6Y+CfSbFP2+4mXoDOrWNvwiYOCZiR5N+6UkUOrsC88KGmoe0nUQdcRKz6dsZrTU2IaXMR1ITYIGIYy1J4KGTraFs7xKFBSX3qWmpsLppInb3bp1w549ezB06FAAQGVlBztTzFi4VFvbSG6/JRguS/BFTSWx/QWAbTn8K80Pqj1IPxeOopbeaqf/dUZgyhPAwruBo38A39xB1xvSgDOeBgqGqbu/WEvNBS6ak+hRtG+CRlnw01kbOmj0QGpXCpK0LQ51zFmNi11HSNAAOpVb72d0o66X4WbnWWj6FHpOWQckANm9aS5wjCkOlI4//nj89NNPGDJkCM466yz8/e9/x+bNmzF//nwcf3yACceMdXSSFFlb8I7G2RC8bXJHyCbZqoA1r9N8JIDmlB1/E1B8euwaEujNwBlPAd/+nZpFmHOAac+1r3baTD06s7L3WmcLlPSpFJCbs9t+nkxZ0QVKamaTvAQByOoFVO7gOWVqEDSUvbaWd5wTdKxJZlHMuty1pDhQevHFF2Gx0BmPWbNmwWKx4JNPPkFxcTFeeukl1QfIko9Go8Epp5ziu9zpOesBcJt0uCzUrjrQwUl7zyZJInVrW/cOtYGHQF3cxl5DZTOxZkwHznoB2LUE6H0SkJYf+32y5KQ08NGZ6KCxIzZQ8REoS5TaNbwzzIYUel4inVeq5vwkv+0aaPHu6r2x2X5nkppHz2d6NzqJF+0cYpY80gvDbx6lAsWBUt++TZNvU1JS8Nprr6k6IJb8BEFAVlZWooeRPJJ1Idh4kyUKlgIFDtbK9nuWtHw78OOLlM0BgK4Dqcyu66D4jsOYDgybEd99suSjNFASBAoKOmoJUkoukF6gfDK3KQuwlEW2z1gFSgCdJU/Ljy7j1dlpDU0nkzQaCj4rd4FPaHYA3r/3OFKcDujbty+qqlq3LK6trfULohjrNFSbn9QBOAPU10sSlT+0N84G4MeXgC9vpCDJkEoB0rmvxT9IYswrklK6jlp+Z8yg7pKRdLwyZ0e+31g3yEgvVKchTGeV0Y0CJC9DavCycNY+mLKi6yYbIcUZpf3790MUW88zcDqdOHz4sCqDYslNkiTs27cPANCnT5/OXX7nsrbfTEksOBsAFPpfZ2sn2SRZAqr3AWWb6evQuqYguHgycPyN3MaXJZhAc5SUiuQxyU7QUqYgUnoTPS8eu/LHalVu5NASz1eKnCE9cBCcXkjdaSN5vZkfjyRDp4nzIuGGdGrekABhB0oLFizwXV68eDEyM5smUYmiiGXLlqF3796qDo4lJ1mWsXXrVgDg11yttuAdhdtG83m8i+ZKEmBJ0mySxwlU7GgKjI5ubpx/1ExWL+Dk2/0Xe2UsUXQm/zPl4eqIGaXMHtGvnWLOAhoiOHCOR8v1RM9X0hppXbh2RQAyu7dxk0BdSSt2gEvwlHOLMixODywODxweEV3TjMhKid3aRU6PBINWQ1OedWYgp0/sGiaFEHagdN555wGg+SnedZS89Ho9evfujRdeeEHVwTGW9LjsrgWZskrmLPrRVgVI7oSOyMdRTy22vYFRxY7WY9ObgbyhQMFw+iocQesZMZYMIg14OlqgZM5WJ7trzqYFo5UQtK3bjcdKouYrmbIaM1o721cGJjU3+Htdb6b5LUpf806qZXDUXKXFCb1OQKpB/b+FBocHZfUOaAQg1ZyC1O49kSZokJgwSUGgJEnUMadPnz5Yt24dcnNzYzYoxtoFj6vjTpCOhjdQkuXkmZD8x+fA6lfR6kyiORsoGNEUGHXpx4ERS16RBjwabTvNEASgNVBrYDXojMrX2on3Ar7phZTpjtf6SoY0CpI0moRmYCQJcEsSfXlkeCQJkiQjxaBDikEHbcvEqkZHz1Uoafl00sxtDX3fTsglSrA4PbA6PHB42u6UKQMoq3OgR3YKjDr1pl/YXCKO1lOHQgk6VBp6oKLGDW2dB5kpemSa9Ugzxvd/tOK9eeemNFdbW8td0Fjnw9mkwLz/0JMlm9RQCvzyBgCZDrC8QVHBcCCje8LS+YwpFk1mSG+Of6CU0gVw2dTNSmQWNZX2qsGcrTBQivH8pJbiOV9JZ6K1h7zlnXozBR8NR2KyO6eHAiGPR6bvogyPKMEtyhDlwMFZvcMDAYBJr0WqUYcUg5YO1NMLw3tfCEJjF7wdqrTMl6TIqmGThSxTcGR1hQ6OWpJkoLTOgR7ZZlXmLDk9Ekrr7I1huQbOjN6QG09MiJKMaosL1RYXdFoBmWYKmlLjEDQp3sMzzzyD3r1745JLLgEAXHTRRfj8889RWFiIhQsXYuTIkaoPkrGYsVZRCUckB8vcFjwwj4Pm/yRLNmnt24DoBrqNobWIODBi7ZU+JYrHmgFHrWpDCcmYQQekogeo2q1OsJTaFTBlRL+d5kxZQL2CRlTxzigBreYriY3Hsq2yKtHQ6IGcfq3LCtPz6aSgShkttyijweFGvcMDtxhZoCIDsLtF2N1UDqYzpsKYkooMpwepBi2EUJ/xehOtr1R/KOjdvFktjy+rRZktt0jfRVGGDECv1cCooy+DXgOjTgt9vJsdhOASJbhF+j3cjcGoS5TgEaWo8oVuUaJgKcsc1b9WtyjjSK0dUuNg3Gk9ILfxeecRZVRZXKiyuKDXUdCUZTbAbFDxBEozigOlN998E//5z38AAEuWLMHSpUuxaNEifPrpp7j77rvx3XffqT5IxmLCaQHqDlLmI7uXsrUxJCl+pRDtUW0JILoSPQrg6BZgz/cABOpax0ESa6+0xugyKfGcp6QzN3Wo0uqALsVA9Z7oSpV1jQe3atMZqNws3M/zWHe8a0vjfCVnbSlK6xzwiBIyzXpkmPXRlz4JGiCnb9vZsqyeVIInt+54HA5ZBixOD+odHthc6mfFbKYCWBoPnDUaIN2oR7pJh3STDhpBgCjLECUZsgyIsgxJliFpMiF7KgCXhX6WZUiSDEkGPKIElyj5DtpDcTcGIZZmCVudRoBBp20KoBq/x5rDLcHpkeASRQqKJAluT3TBUOh9ijja4EBBRmQnETySjCN1dngan3BJlwrRlBXWY90eGZUNLlQ2uFCQaULXdPXXOFMcKJWWlqKoiOqDv/nmG1x88cWYMmUKevfujXHjxqk+QMZipqFxsUG3lf4JZBWFv7aGq6GDr3QfJVdDokdA/53XNC6IPeAMILd/YsfDWDSiDXTi1SJco6MOVc2DOm+wVLU7wmBJaJo3EwumrPADpURklBpZDF1RainzpZRq7W7U2t1INeiQmaKLcGK9QEGtwf/svcsjQacRoNEIdBIxozudWFTA6ZFQ73Cjwe5ps5QuWqIxC5I+1fezJAF1djfq7KHLvgW5K4zW2ogDwGA8kgyPywNbs/OFAgCjXguTTuMXREV6/k6WAYdHhMMtwe4SYXd7IEEPd2oB9LYSVX6PcDU4PDDoXMhJUXYiQZKofM/VrOTPnRbZCZGyOgfcooRuWep+1in+q8rOzkZJSQmKioqwaNEizJ49GwC1jA60vhLreDQaDU488UTf5XbJafE/mJdFoGY/NSLI6BH6HzK3BU9+e1dQRklnAsZek+jRsGSm0SX/ejVRB0qGOPyeAs1xCZSd12gbg6U9yifSpxe0OpBXlTkrZBmWT4ICpSqLE6V1DiC1CEb3bkBueh2tLg+sLg8MOg0yTXpkmPThx5SZRYApEy5P4yR+pwcWpwcekQIbvU6AUaeFQZcCs2SG0dMAvVYDQxt1f5IENDg9qLe7W3VKU58GnpSCiB8taw1wpxZCbwnztY+SDMq+ONwiAArkBKBZxkkLo14Do1Yb8PWTZXq83SPC7pJgd3laZYrcaQUQTdmALEJvjc3csrZUWVzQazRIN4UXWsgycLTB0fh8ENGYAzmKkzpVFhfcooSi7BQK8lWgOFCaMWMGLr30UvTv3x9VVVU488wzAQAbN25EcXGxKoNiyU0QBHTp0iXRw4iON5vUkq2KJh9n96Y65rZwI4fk5nECa9+iyyP/RG1jGWtLdh86UZIMzUfaokbpnM4c22xvZhFgTGv7do2WOktW7w0/g6NPpU5lsaTV04KWoZ4bjT7uM/dlWUZpnQNVlsbUhNYAV3oRDPUHAPhXNbg8EiosTlRZnchonOzeVkDjkWTY9Lmod5lhbWjwO6PfnNsjw+1pDMqkrjDV1QAyNVTQ6yhg0mkFGLRaONweNDhaH7zHiielK+QoSyFFUw60znpo3In5ny4DcHikxiYK9DwLAAzNgidJlmF3UYAV7LmVtWYKkgCI5lwIkgc6e3zXMTxa74BemwKTPvTfSXmDAxZnsxM3ghbu1Oj/1uvtHuwVrejdJQU6FSbyKQ6UXnrpJfTu3RslJSV49tlnkZZGH4qlpaW46aaboh4QYzHXMpvUksdOHXEyegCpAQJCly055t+wtm35grrdpeQCIy5J9GhYMjNl0sF9ateYdfdSRTSNHHzbiGGglJoX+POyJY2WmgZU7w09FqGxRXU85haas0KPR8k8VhWIkoySahsaHP5ZQMmQDkfOIOgcVdDZq/yySwB1I6u1uVFrcyPNqEOmWQ+jTgu7x0MlWi4RNm0m3OmZgFvByQGNDq70HjDU74cMCsyaAqz4nmSQNUZ4zHmqbMuV3h2mGmtMSvCakzUGeFLyoLccRrCW6zKobNHpkdCA8DPA7lT/9uie1AIIkgdaZ3WEI1ZOBlBaZ0ePnJSgDS2qrC7Ut3hfe8xd6WSECuwuEXsqrOidmwKjLromD4Isx6hwNEnU19cjMzMTdXV1yMhQuVtOJyVJEg4epFrlnj17tr/yu8rd4R8smLNbt6NtKOMF65KZvRb4+DIq75lwLzDwzESPqOMSNDS/wx6/f8Sq6zqosXW2ByjfkpxzDzV6oGBY9NuxVQO1B6LfTkvGDMoUKSFJoYOlzKL4ZYNFDy1IHeycfUoXamwQRK3NhXo7rfmSYdKF7sDWBpdHwoEqKxzuEO9HWYLWUQ2dvRKCFN4JPEmfDldG74gDUH3DobgefAfiSu8FyZip2va0jlroLcrmYCkhGrPgTu0OaLTQWctUz/RIhgx6TVuSZRgaDkDjim/GzKTToHtWSsAEbJ3dg/IGh991ssYIZ/YA1U+KaDUCeuemIKXF/D0lsUFYGaUFCxbgzDPPhF6vx4IFC4Le95xzzglnk6wdk2UZmzdvBgBfY492w9mg7IyqvaapFM9bI8/zk5LbhrkUJHXpDwyYmujRdFxCYxmVzkRtp5MxwAjFlNVU0qbV0YkRW1VChxSQWh3rYtH5rnmHOyU0jZ3WavYFLmU2ZsS3ZFarA4zpwcuqg8xPqne4cbTO4Qts6uxu6LQCclINyE4xwKCg45nV6cGBKhvEcNquCRqI5lyIpi7QOmuhs1dAEB1t3l3WmuFK7xnVAak7rRs0bisEKTELGEv6dFWDJAAQTVnQuOqgdam87IeghTu1u18XN09KPrSu+qCvk8KdwN3WXC1BgCu9Jwx1+6DxxG+RXYdHwtEGBwoz/f9mGpweVDS0/r3dqQUxyRyLkoy9FVYU5aQg0xxZtiqsQOm8885DWVkZ8vLycN5557V5P0EQuKEDS24NEaztIzqByp3U9cec1b5X9HZZgKWPUjvcY68KeXa03ak5AGxrPJlzwk2U8WDq0+iofMp78iBZA4xQ0v1LVZCal5y/h1oBjs4EmgGhUiFJoA53ih7fGCxV7/UPUDS6xHw2mbODB0oB5sNYnR6U1Ttgc7Y+9vGIMsrrnSivdyLNpENOigEZ5uBZplqbC4dq7FBc6yMIEE3ZEE3Z0DjroLdXQPD4dxiUNQY4M3tHv2CvoIE7vQcMdXuh2nsp/J23KjFTizutO4QGCRq3OuWpki4V7vSi1vOoBAGu9CIYa3dDjedPNHXxLcwakKCBK6M3jHV7VAzOQrM4Pai0uJCbRr+/3SXiaJ2j1W8s6dNUD3ybk2XgYJUNhVkm5KYpL58NK1CSJCngZcbaFaXZJD8ydUWyVqg6pLj7bR5waB1d3vcDMOQc4Jgr6cx6R/DLG5TZ6HUS0G10okfTMWn01L2sebOTlNzkDDCCMWe3btiiN1EmQ/VmLQKVbdkqI3u4WoGSINC2olnPqGljbXe4Uzomb2bJu4h3ZhE1WIg3UyaCBpLNDkYdbhFldY5W84faYnF4YHF4oNMKyE4xIDtV32ruxNF6B8rro8/SSMZMOI2Z0Lit0NkqqFGBoKXSLJXmgEj6VHjMXePeLCBkUBANjQ6uzD4QPA7oHFXQOmrQsmFGeAR4UvLhSWl7DpWsM6vz/AlauIPsx0ejhTOzD4y1e8Iu0VRDjc0Fg1YDo16DI3X2gH9ZsQp8WyqtpTbkStuHR9J0n7H2KZJsUktiYkoNVNFQBvzxX7qcNwQo30pND3Z9B4y+Ahh6ftwnK6vq0Hrg4M9UEjbu+kSPpmPSGihIavk+MaRQd7J2k20VWmeTvFK7qh8opeYCmT0oiI9kPpcajRx821IpUArV4U4JQWjqPChoKHOfCBotYMpoCtha0hnh9Igor3ei1hZZ8wKPKKOiwYmKBidSjVrkpBqQbtLjcI09rLV/lJD0qXBlpkLwOABZUj3AoBKyBgiiXdXttknQwZ0S4w6IAGSdCe607nCnFEDnqIbWURV2cCFrjXCl9wyrxbUnJQ9aVx2EKI4rPOY8ysCGQ6NvzCztbdUAJJbKGxzQaoSAC/hG2w5cKW/78Ext+Jk8RYGSJEmYO3cu5s+fj/3790MQBPTp0wcXXnghrrjiiognLTIWc1FlkzqIde8AopsyLWe9CBz5DVjzOlC1izIxW78Exl4H9Ds1Pl2m1CSJ9LsAwJBzO15JYTLQGhuDpDba8abmArXtJFAyZ7d9UsCUQZkDj0olKoIWSGucP5DVkzpmhtsa2/t4NU9gqHFQEm6HOyWExoVPE91fypQVMFByC3qU1zlQY3WpNkSrU4TVaYcgRFBqp0DMMjC+ErJdiH0JngautO7Rlw0q2qUWnpSu8JhzoXHVQWevCjrPRzTm0GKp4ZZ8Cxq403rAULcnouFR5z9l8/hknQlOb7AUUbZMORnUjr4VQUtzk+Ks3u5BtTP8/1VhF/DLsoxzzjkH1157LQ4fPozhw4dj6NChOHDgAGbOnInzzz8/ogEzFhdqZJPas4odwO6ldPn4G+mgpPsYYMabwMT76CC3oQz4/nHgq5uAss2JHa9SOxcD1Xto7tUxVyZ6NB2Pzgzk9m87SALoAFOI40FMxIJkk7xSu6q3u7R8ahQANGVOtAoCH7UbMES7PWMGkNldnbG0JAhxX6eoFVOW70BXkgCHW0KlxYV9NR5UW9QLkppLdGwYDVlnimrR17D2oTXBmVUc03ksQQkCJGMWXFn94MzqD9GYDSrR9N6ugyu9F9zpPRTPi5X0qRBNkTUtibQBgqxPgSujJ/x+hwRQlA1TmcMVfj+FsEc4d+5c/PDDD1i2bBkmTZrkd9v333+P8847Dx988AH+8pe/hD9SxuKhs2eTZJkyRgBQPBnIHdB0m6ABBpwB9J0IbPoU2PghUL4NWPA3oM94KmHLiNFBkVrcNsqWAcCYKxrnGTDV6FOocYM2xL8LjQZIyUn+eXwpXYIHfABgzqElAKQoy1O0htZBl7axAULlrvDWbUmmQCnSDndJTJJkuESpcd0aES6PBNGqg2Sr8TsLLpl4eZG2eFK6QuNugMatIFMaJtGUS3NYkqTKQdaZ4U4vgju1ADp7FQTRSVmkKOZ+uVMLoHE1KOoiKOlSowocJUMG3Gk9oLeURLyNaESSDUuUsAOljz76CA888ECrIAkATj31VNx3332YN28eB0qdgEajwXHHHee7nPQ6ezapZA2V2Wn1wNhrAt9HZwLG/AUYdBawfg6wYyE1eziwmuYujb6CSpKS0e+f0LyP9G40VqYefSq1AA+33CUlN8kDJYEyPKFoNBRQWaL87EgvDJwh0ZtpIdXqfQhZsqTm/CSAXkutUfl8S50ZYnZfaONZ+hQhSZIhyjJESYbk/S4BoizDI0lwizKcbhEuUYLb0/r51whpMEj+c8mkAB3vWBNXehH01lJonXVQpQxPoMVtJUOS/t/R6OFRq2xM0MCd1h2G+r1hP8Sd1i3q3YqmbAiSBzpb/NeFjFU78FgIO1DatGkTnn322TZvP/PMM/HKK6+oMiiW3ARBQH5+7CdUqqKzZ5MkD7CmMZs07EIgPcQHe0oXYPxdwLAZlIUqWQts/gzYvQy44G26PZlYyoHfP6bL4/4asH0vi5AhnbqRKTkZojfR45L1by41N3Q2yXffrvT+ivSgT2emDFtbTJmUra0/FHw7sVj7SG9SFCiJuhSU6bqhptyOVKMbXdIMyDAloCtdCxanBxUNTnhEyRcYyXL0pWySIYPKSJtl/GQl5ZKdkUYPd3pPeFKc0NnKoXXWItK/HUmfDld6D9U69LUHkiENojEnrIV8RWO2ag0QPCldIQsCNB47IEsQZBmABEGWqPmMLNNlNP6sQhAc63bgags7UKqurg56cJyfn4+amhpVBsWYahrKEj2CxNrxP6D2AM0rGHVp+I/L6Quc+Sy1Ev/xJaD+CLDxI+DEW2I31kise5cO+PKHAX0mJHo0HYcxg+bSRJIxTu2SnIGSoAkvm+Sl1VP3NXuE/9cywjjjm9aVmka02TZcCLrIacT0KW13dmuhTjThsJwP0UUTv71tro16DXJSDchJMUCjie+ZYYdbRGmdA5YwW3MrJmggGtIbD/ZJq7VwWECy1gh3ehE8KXnQ2SqgddYg/INrAZ6UAnhSVJwj2I64UwuhcVtCdNjTwKNy5z/RnIuwZ+zIMiCL0LrqobNVRLDosAB3avTZsHgK+7+gKIrQ6dqOq7RaLTye+LUbZIkjSRJKSkpQUlKS3OtqORuUdZfqaNw2YP17dPmYK2nVeaV6jAVOvoMub1uQXGvlVOwAdi2myyfc3G7S+HEnaPy/ILT4asGUqTyT5Pf4rOQ8E5ySq3xtnkibOhgzwi9VzexB9w9Eb47N+zqM4MvpkXDApsdBFEKUW4/B6ZZQWuvAtrJ6lNbZ4fLE/n+ByyOhpNqGXUctsQuSGonGrGY/CZxRUogCph5wZg+EaOqCUI0DZK0RzqziThskAQA02pAldR5zbmKDdkEANDqIphw4cwZSK3Rt+Nkt0ZQTuy6MMRJ2RkmWZcycORNGY+APC6ezHa8vwxSRZRkbN24EAHTrlsRnBjp7Nun3T+hseEZ3YPA5kW+n+7FA3mBq8rDpU+qal2iyDKx5jS4Xn07jY62l5YeX2VCTIFDJWbTze9SkNJvkZUilTopKT7goec69bbErd7ZuSR6Lsjsg6LwnUQKqrU5Uy2lwpfYIGahJElDZ4EKVxYV0kw65aUakGtXtZCVKtPZQpcUZtw5xkj7dV37H2aTIyVpD45pEedDbKqB1VKNlW2rFbbU7MMmQAdGY3ZiJa0HQB13ENhEkYxacxixoXBbo7BXQuINUEwjauKyDpbawP82uvDJ0y11u5MCSRmfPJlkrgU2f0OXjrotulXtBAMZcCSy6D9j6FTDyz4lbENLrwE9A6e80J+m46xI7lmSlNTat3xNvKbnJFSildg3dta/Nx+a2+iwRJcAjSTDqAhzYmXOUBzgaLWXxKnf6d9pTu5GDl85AbXlbdPWrt3tQZXXCaeiieLK4LNPj6+0emA0adEk1IitFH9X6irIso8rqQnm9E2KgdVhiSRAgGjKhdVZzNkkNGj3cad3gTukKnb0SOnsVrcOU1h2SX/aOuVO7QeuyALK7xfX5SRtMSoY0uAxpEDx2mqPmal3am8h24NEIe8Rz5syJ5TgYU1dnzyZtmENnp/OHqjN3p2gctRWv3EnNHRIRnNiqqLlEyS/0BQDDL4osU9AZZPZI3Jo0OgOVkznrE7P/5gRtdO8RUxYF5GLTvIEqqxP1djfSTTrkpBmh987RETSh12hqi85ImaWqPfDN6YhVRgmgZhONc8mcHgnlDU443CI85ryou3nZXRIOuewoq3fArNfCqNfAoNXAoNPAqNPCECjAbKHW5sLRemdcSvraIhopUJI4UFKPRg9PaiE85q4QZImzdYFotHCldYOh4YDvKllrgmgK0hwmScg6M9wZveARndDZKxsziHK7agfeUvsL7RgLpbNnk6r3URMHABh3ozpzHASB2od/9w9gy3xgxCWxbxcueajcr+QXCpAqd/rfntNPWYOKzsScnfh27qm5yREopeWF3948EEGgjFT9YQAUVNTb3ZAB1Ds8aHB4kJNqQJbZAE1G1/C76gViTAcyi4C6g/SzSp2tAtKbIToaUG11oq7x93GndoOo4sGMR5TRIHrQ0KKiUBDQGDT5B08GrQYuUUJZnQN2BQtCxoqkTwMEHWeUYkGjU6OJeIclGTMhOjN9mZn21gBB1hp9JZc6exUkfUq7nUfMgRLreDp7NumXN6mNZ5/xQMEw9bbb6yRaU6dqD/DHf4Fjr1Zv2162auDQWuDgL8Dh9RT0Ntd1IFB0PFB0HNB1UHQHwB2VoE2ORYJNma0yMXGn0UXekKG5lC60AK1MmZfmB3gygCqrC7VOGdmZ2ciOdl+pXSgb7KyPWUZQlGRUOwVYqq2Ni6oKcKf1gGiKevRhkWVqBuF0J3EzIIDK74yZkDWc9WDx507rDm2NFZIuBZIhLdHDiYya600lCAdKrGNx1HfubNLhDbTArKAFjvurutsWBGD0X4CljwCbP6eyt0g66bUkeahJxN4VrbNGxnTqvFc0jr4HW5eGkYzu0c1JU1NKLtBwJHH7T40ym+Sl0QIpXdBQVQqHO3Cmw2HsikN1LlTZPSjINCMtmoYGmd0Bm/rZJJdHQqXFiWqrC3BrYWwMklzpPdvVuibxJBoyIek4o8QSQKODK60bZG376hLX0XCgxDqWZJpALnmAH14AIAHHXR/7g3xZalpcdsi5NEdFbX1OoXkUNfuBLV9QOV601r4DbPq46efcARQYFY0D8ga1y8mfCWNIo4xEskjJoUxMIops1MomNZLMuai07A94m6wxNrZApvk5+yqsSDfpUJBpgkkfYaCm4ueFzeVBZYML9Q53U9c4rREQdBQktdez1XHAzw1LJG50kXh8BMIU02g0OOaYY3yXk4atOrmySQd/BnY2zhUqWQtMuAfoeULs9rdrKVC1C9CnAsfEqAOloAFGXwF8/zg1dRh2AbVQjtTBn5uCpHE3AP0nU5kTi4AQm+A4Glo9leA5aiPfhimTMlNuO60N5rbTIsOhpOWrWrpWbgdc2nRopNbzrqgblX/9fYPDA4vTguxUA/LTjdBp4/9ZWWd3o9LihM0ZIAsmCHBm9eP5N4wxFgQHSkwxQRCSb/0k0eObbJ00tnxF37VGWs9o0f2U6Tn+xrAWfFTE4wTWvUOXR19Gnbpipe9EYMNcoK4E2PIl7S8SlnJg+VN0eegMYOSfVBpgJ5WWF9suaZFKzY08UErpQs0NBMG/OYUkNgZOzYInjwO+zJVGT8GVSpweEZUWJwRzLgxu/0BJ0qW2edZXloFqiwu1NheyUwxIMWhh0msjzzKFQZJkVNtoXaNQHeM4SGKMseA4UGIdQ11JqzVBEqq2hJoRQAAueBvYuoAaIGz9CjjyG3DqP6jETC1//BewltOcjGEXqLfdQDRaYMwVwPInaa2mYecrX+9F8lBWyllPz8PxN8RmrJ1FItdMCsWYTicGWi6mGkpaAZDRRqttjRYwptGXlyQBnsbgSatXNZtUWuuALAOyIQ2y1gRBbPpdwpmoLElAlcWFKu/wNYBJr0WKQQuzPrrgSZJkeCQZHknyrYMkJXmPBMYYay84UGKKybKM0tJSAEBhYWFUCwqqwl4bXWlPLGxtzCb1PAHI6gmceAvQcxyw4mmg9iDw5U3UNW7EJdFPNrfXAr99SJfHXkvrscRav1OBDe9TFm/rAuXZoA1zgbLNFGCd9jB1R2ORS+SaSeFIyQXqD4V//4weQJrC+UUaDZWBRlMKGkC9w40GR9NJGI/5/9u77zi56nr/469zzvS6vW+STbLpIQFCCS3SIdIUFeWqWK8FFK7XgnivgCKI915UVETUH9hRQRQUEJQQOpJASAiQXjZle50+p/z+OLuzJbubLbP983w85rFTzpz57p7d2fOe7/f7+RbijNQAYLpCmM7hv55pQixp9BoS1194Mi2LtGFhmBa6YZI2LQzDIm2a6IYdjiQUCSHE2JnE/1nFZGWaJhs3bmTjxo2YE/1f2jSgbRgnYOMhHe+em7T00u77K06A9/wc5pxu96j86x742xdGX4Di1V9COgr51VB9zuj2NVSqA479oH198++H11twYAO89hv7+hlfnHzzasaLoto9QaPlyZn4NZOOxpc3xBXlFbtYyHBD0hixLIvatt6/24Y7BxQnoJD2j3Bx2X50hafGjhQ1zXF21EXYVR9lf1OMgy1x6tqTNEdStMXTxJIGKV1CkhBCjDXpURJTW9sBMNMT3Yredv0TUlEIldnhqCdPDpz7DXtB2BfuhMOvwwMfg9O+APPPHvpr6Elo2gkNb3f3Xp38mSGejGZJ9bnw6i/sdaveesQuF340sSZY9y3AgsUX2z1TM43qtOft+Ars3sRIXefaXyOoDKdoUyNoqpr9ux9vHngbRYO8quyUnM+ShkjyyLV+FAXdm4diGjLHR4hhsiyLHfURnt3RQHM0TUWul1l5Pmbl+SgNeyak6IkQg5GgJKauRPvgJ14TwbLsAgdgF27oL7goCixaC6XH2KGh/i17vs7+l+C0a+0Szz2l43YoatwBjdugYTu07rPLgXepPBnKjxuzb6tfqgNWfhCe/V/Y9DtYfMngw/5MA576ll3YIm8erL5m/No6GTg89hwyX17vCmnBEjtEtO63ewaHI1Q2oWsmGaZFfYfd4+JQVZyagkNTcagKTk1FU3t8n/6Cgf9eVYf9O+Ea5ly3MZQ2TOrb+6+up3ukMqMQw3GwJc767fWs397Aobb+RyA4VIWyHC+VeT5m5/mo7BGgnBKgxASRoCSmJtO0CzhMNvVv2qFGc8GCCwbfNlwBl/wAXv0VvPYr2Pkk1G62F4qNt9iLrzZut0+grX7G2HhzoWChvdbQUHpzxsKC8+2hf9F6ePtvsOzdA2/72q/h0Kt2YDjn6+Mzl2oycAXtoWSeQRb0dHqgcAFEGuwFWvs73kc8x2+HjwmSSBvsb44d2ePSg6KAU1NxaApOVcWTUHGYCYIeJ86uEKW5IX/epPt9qG1LdK851Jes7SXEUTVFkjy7s5H12xrY2dC9dIfLoXJyVR5zCvwcbIlT0xKjpjlOvPM9ZX9zjOd77EfrDFCz83wsLQuxoiKHilzvxM+PFjOCvNuLqanjEBipiW7Fkbp6k+adNfiJcRfVAas+ag/RW/cte3HOp7555Ha+fLs6XNelcIE9dGui/1FoTrs8+HPfhdd/C4sv6r8ww6HX7GF6YA8zzJk9vu0cdwp4c+wepOH0knQFqrYauyLgYPvPqRxtI0esJZriYGt84CDRybIgpZukdACDiBnCGWmnOZIi7HOSGwrhKKie0F6x/kSTOq2xSTakV4hxEkvpvHW4A5dDJeRxEPI6CbodQxoWF0nqvLCrkfXbG9hyoC0zoFhV4NhZubxjQSEnVeXjdfUuYmRaFo2RJPubY9R0hqWa5jj7m2PE0wY1nfc/t7MRgDy/i2MqwqyoyGFFRQ6FwfH/oMW0LFpjaQJuBy6H9HhNVxKUxNSTjEC0YaJbcaR4K+x+2r6+9LLhPbdkGVz+M3j5bji0ya6U1xWIChZM7kVYF1xg94pFG+25V0su7f14vAWeusXuJVlwISw4b2LaOR4UzT5W/kJwjLCSn8Nl97DEmu2qgv2VvZ+gNZMsy+JQW4LmyMg+pDDcOTijh7Esg+a0m3q9hMKoQUHAgapOnk+HD7fFJ7oJQhzBsiz+tbeZv24+jKoonF5dwOq5+fjdoz+VsyyLt2s7eOLNWp7b2Uiin55iv0sj5HUS8jgJehyEPE5CXgdBjxOfS2PzgTZe2duMbnZ/grK4NMSaBYWcNr+AsHfgD0RURaEo6KEo6GHV7Lxe7WqMpKhpjrGzIcLrB1p563A7zdEUT29r4Olt9rlAWdjDiko7NC0vDxMa5LVGq649wT/equMfb9XTGLGH53qdGjk+J2GvfcnxOgn7XD2uOwl77K85Xqf0hk0himUd7TPBqa29vZ1wOExbWxuh0CSvDDVFGIbBo48+CsDatWvRtLFbPPEIpmnP0xnumizjYdNv4F8/hcJF8K67J7o14+uNB+GFH0CgGK74dXcPgWXCY1+BA6/YvUjvuntyLoqaDa6gXYxgtOXeezLSdsGSnuXvNbf9OzbO5cBTusn+5ijx1OhKrTkjh1DMFKng7EyPqKYqFIXc5PtdE34C0RRJcqh1Er6/iBnLsixe2tPM/a/sZ3dD73mMTk3hxDl5rFlQyKo5ecOey9MaS7FuWz1PvFnHgZbuDwiKgm6cmkp7Ik0koQ+r1MysPB/vWFDI6QsKKQlleXF17AWg3z7cwesHWnn9QCs76yP0yGYoQFWhnxUVORxTEWZJaQifa3RhMm2YvLS7iSferOP1mtaRlN7JcDtUSsMeSsNeynK8lIY9lOV4KQt7yJsE74EzQaSjnVOWzBpSNpCgJIbNNE0OHjwIQHl5Oep4nrC1Hxp9Oe2xYBpw/5V229Z8BRZeONEtGl96En73frv36IwvwaJ32vd3hUfNDe/6MeTNndh2jhXNZc8X08aokz7e2l3hMW/euJcDb0+kOdAcxzCz8O/C1Aec4+N02J8q5/pG9omrYVrE0wa6YRIY4lChvs/fVtuRne9TzFiWZWXlZNe0LF7a3cT9r9Swp9EOSF6nxtrlpXidKk9vb+gVbvwujVPmF/COBYUsLQv3LqbSg2FabKpp5Yk3a/nXnu4eILdD5bT5BZy3tITFJcHM92CYFtGkTnsiTXtCpz2epqPXdfuxilwfaxYUMiffN64n+9GkzhuH2ni9ppXXD7SxvznW63FVgflFAZaXh1lensPi0uCQg9OexihPvlnL09sa6Eh29+6vqAhz3pISTp6bT9owaYunaY2naYul7K/xNG2xdOZ612MdRwmd/YUot0MlnjaIpwxinV8ztzPXdeJp+3baGN6HWQoKfreDoKfz4rZ7CTO3M9ftIZh2r6JjSgc6CUo9SFCaRlIxu7jBqD7LGSP7XoC/3wDuEPzbHyfdxPRxsfkP8NJdECyFK35lV/N75Fq7V6lneJpuFNVew2qsK7aZhh2Y/OM7DLOuPTFg9bex4naqFAc9hH0DD58xTItY58lBImUST9trC/Xkc2uZ4UFux9F7+g62xkc8rFDMXPUdCbYcaGPLQfvSFE2xqCTIioocVlbmUF0UGFZoNy2LF3c1cf8r+9nbZJ/0e50aFx1TyqUryzND2CzLYk9jlKe3N/DM9gaaot2/u3l+F2dUF7JmQSHzCv0oitLvkDGA6qIA5y0p4YwFBaPueZkMWqIpXj/QyubOY1Lb3ruHWFWguijYGZzCLC4N9ZozFU3qPLOjgSferGNnfXcRioKAi7MXF3PO4uIR95SlDZOGjiSHWuMcaktwuOtrW5y69gRT5TOa42fncsOFi6fs3CwJSj1IUJomLMsOSenY0bedCI99GWr+BcdcYa9nNBOl4/C7D9jDxE7+LGz5oz2XbP45cObXJr7wxFjJmW2X/J5mdMNkf3OMaNKYsDZ4XSpFIQ8+p2Z/ajpIKDoaj1Ml7HUS8jrxOI8MTYm0wc76yFELVAjR0JHsDEWtbDnYRt1RPkjwOjWWl4dZUWkHp8oBKraZlsXzOxv5/Ss17GvuDkiXrCjj0pVlBD0Df3BgWhZbD7axfnsDz+1q7PV3W5HrJc/v6lVcIeB2cObCQs5dUkJVgX/4P4QppL4jwRsHu4Ns3+OlqQrVnT1OTZEUz+1qzLy/OFSFk6ryOHdJCSsrcwbspcsG3TCp70hyqC3OoVY7PB1qTaCbJl6nhtel4XM5Mte9Tg1f13VX93WXQ0Vh6O00LYtIUifS2TMYSeqZXkL7Pp1I0u457EjoRDp71lbPzecrFyzK+s/k4dcP8sDGAxQFPczKt0vEz+4sFZ+toYkSlHqQoJR9lmVRX18PQFFR0fh0v3bU2hXhJqP2g3D/vwEKvP/XECqf6BZNnE2/hX/d0307XAHvumdSrY+TVf7CqbHg6zBFkzr7m2PoxvT89+ByqIS89mT0ronwuxoixCYwFIrJqyliB6PNB9t442Abh9sG6aGoCFMYdLP1YDubDrSyuaa115AtgDyfixWVYVZ2Fh/I8bl4fmcj92+ooaYzIPldGhevKOPSFeUEPMPr5UkbJhv3tbB+ewP/2tNMqsdQrJ5DxqZqb8Bo1bcnMqFpy8E26juODLqVeT7OW1zMmYuKBi1CMRNtOdDK1x/eim5avHN5KZ86Y27WzgMffv0gP312z4CPB9wOOzj1DFD5/mEfIwlKPUhQyr5xL+aQTkDD20zKIXcAL/0YNv8eKk+CC2+f6NZMrFTMnquUbLcLOlx6FxRUT3SrxoYraFenm0Y9ZWnDpDWWpq59kDWEphmHpuB1anQk+qkuKGactniaXfURdjVE2NkQYWd95IgT6e45LzmdQ7cGnvNiWha7G6K8fqCVTTWtvHmovVdwAQi6HZkw5XdrXLqinItXlBHIQjW7WErnpd3NtMZSnDKvgJJw9osrTHV1ncFp66E2nJrKWYuKWFgcnNJzcMbaczsb+c7jb2MBHzp5Nu9bNfrlKh7dcpgfr98FwLuPLWd+UYD9zTH2Ndnl4g+3xQccmpjjdXLCnDwuXVnG7Pyj95AOJyhN/cGoYvpr3c+kDUl60i6JDUeWxZ6JXD5Y9TF44U449T+mb0jSXJA7Z0qFJMO0SBsmKcMkrZukjR63DRPdsGZMOOpJNyw6DAlJM1FLNJUJRLsaIuysj/aau9NFVWBeYSAzp2VJ2dCrqKmKwvyiAPOLAlx+XAUp3eSt2nZer7GD0876CB1JnYDbwaUry7j4mLKslPvu4nM5OGtRUdb2Nx0VhzwUhzycs7h4opsyZZw2v4CW0+dyz7O7+dVL+8jzu0b183vizdpMSLr8uHKuWj3niKCa0k0OtnYHp66vte0JWuNpnnyrjiffquPYyhwuW1nOsbNyshJ2JSiJyS3SAOno0bebKLuesntPAsV2j5Kw15BafNGAlc2mPEWF3Kqxq3A3iETaIKmbWJaFadmfVpuWHXDMrvs6P3Lruq13hiFzdFW9hZjSmqMpdtZ3sLO+KxhFaY72X7ijPMfLvMIA8wr9zC8KMK8wkLXw4nKomUVSP7waOhJp9jXFmFvonxaFFMTMcfGKMpqiKR589QA/eGoHOV4nq+YMf77uU2/X8cOndgJwyYqyfkMS2H87VQUBqgoCve5PpA121HXwty2HeXF3E6/VtPJaTSuVeT4uXVHGmQuLRjXMVP4qxeSlJ6Hj0ES3YnBv/sX+uuSS7K6fM9VN15AEEK6ckDlXDR3JGTUkTkwuh1rjbKppZfW8fHJ9I1xMeZy0RFPs6Bo+V29fmmNHhiIFu9DBvKIA8wvtQDTegSXocbKsPDxurydENl21ejYt0RRPbavn24+/za3vWs6C4uCQn//M9ga+/88dWMDa5aV84rSqYfcCeZwayytyWF6RQ217gkdeP8STb9ZR0xzjh+t28quX9rF2WQkXLi8d0XvXND6bEVNaKgrNe+zS0mPJNEYecOrftudOqU5YOE1LX4ve/IUDVrhrjCSJpwzKcrxZrQJkmhYHWuK0xdNZ26cQQ2FZFpsPtvHwpkO8srcZC/jVS/v4xGlVnLVonAr5HEVLLMWu+kgmGO2oj/TbU6QqUJHrY36hPQxuXlGAuQX+fisgCiGGRlEUPnfWfFrjKV7d38rNj2zlO5evoDz36AvLP7+zkf97chumBecvKc5KUYiSkIdPnj6XK0+cxRNv1vLI5sM0dCT53Ss1PPDqAd6xoIhLV5aRP4y8JEFJTD7xVmjdN/Yh6V8/gy1/gJVXwrEfHH4vyJt/tr/OfQd4c7LcODHpuIIDVjSMJHVq2+zenmhKpzzHO2gp36FKpA32N8dIpmXcnBg/acNk/fYGHn79UGahU4B8v4umaIrv/XMH67c3cPWZ8yke4Xoyo9EV4P64oYbXD7Qd8biqQHmuj+pCOxBVFwWoklAkxJhwaCrXX7CYGx7aws6GCF9/+A3+9z0ryPUPnEZe3tPE/zxhh6SzFxXx2TPno2bxgxe/28G7jq3gkhXlvLCrkT9vOsj2ukhmHtMxZYGj76TThFa9e+aZZ/if//kfNm7cyOHDh3nooYe47LLLMo9blsXNN9/MPffcQ0tLCyeddBI/+tGPWLp06ZBfQ6reZd+YVr2L1Nvltsda0y740ye7w1jxMjjrvyBYMrTnJ9rgN+8FIwWX/giKh/47KaYgzQUFC/udl5TSTXbWRzD6lOPJ9TspC3tRR9i71BZLU9MSk6F2Yty0xlI89kYtj245TGtnD6bboXL24mIuOaaM4pCbP286xG//tY+0YeFxqnz45DmsXV46puvLdDEti1f2NvPHDQfYVtcB9B4+V905n2huQaDXAqJCiLHXEkvxlQc3c7gtwdxCP7e9a3m/w1g37GvmW397C920WLOgkP84Z8GYv39YlsXbtR38edNBXtrdhGnBvtsvmvxV76LRKCtWrOCjH/0ol19++RGPf+c73+GOO+7gvvvuY8GCBdxyyy2ce+65bNu2jWBw6GMgRXYpisLy5csz17PCsqDtAMQas7O/o73Wiz+0Q1LhQmitgbo34MGPw+n/CfPOOvo+tj1mh6T8aihaMvZtFhNnkOINpmmxryl6REgCaInaC/dV5PqGVebXsixq2xM0dvQ/0VxMrFhK57bH3qYpmmJeoZ/qogDzi4JTehjX3sYoD79+iKe315PuXDurIODincvLOH9pca/e0fccX8Hqufn8YN0Oth5q555nd/PMjgY+f1Y1lXljM3fPMC2e3dHAAxsPZBZidWkq5y0p5l3HllM0Ab1aQojecn0ubr5kKV96YDO7G6Lc+uhb3HjxUpxadyGFTTWt3PqoHZJOnZc/LiEJ7HPVxaUhFpeGqG1P8KdX9nL3UJ87WdZRUhSlV4+SZVmUlZVx3XXX8ZWvfAWAZDJJcXExt99+O5/61KeGtF/pUZoCTANa9trV48bDnmfhyf+21/l53y/t4PTULVD/pv34ggvglM8PPGHfMuH+D9qFJs74Iiy6aHzaLUZHc4OpgzXMRUVzZg84L2l/U2xIc4fyAy5KQp6j9i6lDZP9zTFZ+HSSShsmNz+ydcDhXhW5PrsUdKHduzFnEocnw7R4dX8Lf9l0sNf3s6A4wKUryjllXj4ObeBKUaZl8fgbtdz3wl7iaQOHqvD+Eyq5/LiKQZ83HGnD5J9v1fPgqweobbcXefU6Nd65vJRLVpZN+qISQsxEO+o6uOHPW0ikTdYsKOQL5y5AVRS2HGjlpr++SUo3Oakqj+svWJS194rhmhbrKO3Zs4fa2lrOO++8zH1ut5s1a9bwwgsvDDkoiUlOT0HzbtDj4/N6RspeIBZg+RUQLLWvX3InvPpLeO3XsP1xqN0CZ/03FC06ch81r9ghyeWHeWePT7vF6LgCkD/fXvfISIOesH/39IR9MVJ2lcW+63UNUryhviMx5AILTZEUHQmdilzvgGWGo0md/c0xdGNSfHYl+rAsizuf2sHrB9rwOFU+dfo8GqNJdtZ3FxDY32yv6/HU2/WAHZ5m5dnhqaogQGHARX7ATb7fRY7PNWafpMZSOk3RFM2RFE3RFE3RZOZ6c9ftaCqzeKOqwOp5BVy6ooxFJUNbaFNVFNYuL+WEOXnc9fRONuxr4dcv7+e5nY18/qxqqodR+aqveMrg71treWjTwUxhhpDHwSUry3nn8tKsLMQqhBgb1cVBvnrBYr7xtzdZv72BXJ+Lk+fm8Y2/2SFp1excvjKBIWm4Ju27TW1tLQDFxb0XsCouLmbfvn0DPi+ZTJJMdi8Y194+Tr0UM4hlWTQ3NwOQl5c38uF3qZgdksxxrOa15UE75Pjy4dgru+9XHfZCqeXHw1PfsudJ/eVqOOETsOIKe/hVl64iDgsuBOfRK7uICaY6ei8Oqznti7vPdpZlhyUjaX819e4g3Ud7Ik1d25ELUw4mpZvsbohSEHRRHOzdu9QYSWaKQUw1pmVldRLuZPXLF/fx9LYGVAW+csEiVs3uHaDtdXoi7KzvYEfnWj2tsTR7m2LsbYoB9b22VxXI87vI97vJ87so6BGi8gNucn1OdMMinjaIpYzOrzrxzHWDeKrrMZ1YyqAtnqYpkiKeHlqPpN+lce6SEi4+pnTEw9cKg26+ftES1m9v4J5nd7O3KcYXH3idS1eWc+WJs47ao2ZZFkndJJYyiCZ1ntvZyCOvH6IjaS8CnO938e7jyjlvScmk7Z0TQvR23OxcPn9WNd/9x3b+vOkgf918CN20OLYyh69euLjXcLzJbtIGpS59T8Ityxr0xPy2227j5ptvHutmzWimafLCCy8AoyjmkGizh9uNdWW7nmLN8Nqv7OsnfhKc/QytK10B7/k5PPO/sGc9/OsncOAVOPOrdu9Cx2HY/5K97ZJLxq/tYoQUOyRpQ6hApyjg9NiXQSTSBjWd8yRGorGju3fJ49A42BqnNTb1Sn8ndYN7n9/LE2/WEnA7KMvxUt556bpeEvZMqX+IA/nblsM88OoBAD53ZvURIQns0HNiVR4nVtmPWZZFUyY8RdjfHDuiN6cxkqIxMjZz0XwujXy/KxPG8gOu7ttZ7tVSFIV3LCzi2Fm5/PTZ3azf3sBDr9mTpk+qyiPWGejscGeHuliP4NfPFD9Kwx7ec3wFZy4smha/Q0LMNGctKqI5muIXL+5FNy2OKQ9zw9rFo1r8dSJM2qBUUmJXH6utraW0tPtT3fr6+iN6mXr66le/yhe+8IXM7fb2diorK8euoWL4Ig2dle3G+ePzV34G6ZhdwKH6vIG3cwfhnJvsgg0v3AmHXoUHPg5rvgR1bwKW3fOUM2u8Wi5GKlhqH88sMUyL/c0xzFHm+2Ta7l1yaAppfep1I+1vjvE/f3+7s6cEWmJpWmJpth7q3YOvKlAU9HQGJ08mRC0sCY7rop6j8eLuJn6yfhcA/3bSLM5ZMvD/n54URaEg4KYg4Obkufm9HjNMi9ZY57C4SLLza4rGHkPkWmIpnJqK16nhc2l4XVqP645e9/s6Hwt5nXaPlN89IVXfwl4nXzxvIWdUF3LX0zs53Jbgz5uGtmi4gh3uynO9XLaynFPmFYzLJG8hxNi5/LhyXA6Fw60JrjplzpTsFZ60/6mqqqooKSnhySef5NhjjwUglUqxfv16br/99gGf53a7cbv7jqkRk0bbAYg2jP/rNu6wgw/A6mt6D6Xrj6LAorVQsswu9NC4HZ74b3txWYCll41pc0UWeMIQHNpJ7VDVZHFNI8tiyoUky7J44s067nl2NyndJMfr5HNnVZPnd3GwNc7BlhgHWxMcao1zsDVOPG1Q256gtj3Bq/u79+NzaZy3pJiLjimbkHV4huqtw+3879+3YWEviHjFqux86Kapit2rE3DDKObyTFYnVuWxtOw4Ht1ymGhKx+ty4OsMdvbF0Svg+VwOPE51UixgK4TIHkVRuGRF/+sPThUTGpQikQg7d+7M3N6zZw+bNm0iLy+PWbNmcd1113HrrbdSXV1NdXU1t956Kz6fjyuvvHKQvYpJq/3QxISkrnLgWHbp75LlQ39uzix7naRXfg6b77fnU/kLYdbqMWuuyAKHx65Wl0W1bQk6EnpW9zmVRJI6P1y3k+d32iX8j63M4T/OXZCpPDa/qPcCfpZl0RpL2wGqNZ4JT3sao9R3JPnzpkM8/PohVs/N57KV5SwqnVxVSQ+0xPjmX98kZZicMCeXz7xjvpzID4Pf7eC9WQqWQggxUSY0KG3YsIEzzzwzc7tryNxVV13Ffffdx5e//GXi8Tif/exnMwvOPvHEE7KG0lSV7JiY192zHg6/bpeHPmkE1RI1J5z8aahYBZt/D0vfZRcIEJOTotrzktTsdfG3xdI0dAyveMN08vbhdv7niW3UdyTRVIUPnzyby44tH7SIg6Io5Ppd5PpdLCsPZ+43LYtX97Xwl9cPsammled3NfH8riYWFge5dGXZpBhy1RJNcePDW+lI6iwoDvDl8xdNeJuEEEKMv0mzjtJYkXWUss8wDB599FFgGMUcLMsOK+M9L0lPwh+vgo5aOO4qWPXR8X19Mf4GWfdoJBJpg531kSlZkW60TMviwY0H+PXL+zAtKAl5+NL5C1mQpeFiXQudrttWj252LXTq5uJjSjlvacmElIGOpXRueGgLuxqilIY9fOfyY8iR9XqEEGLamBbrKIlpJh1j3EMSwJYH7JDkL4AV7x//1xfja5B1j0ZCN0z2NkVnZEhqjqa448ltmcVIz6gu5Ooz52W1CMOcAj+fP7uaD62ezWNbDvPoG7U0RpLc+8JefvfKfs5ZXMwlK8ooDY9PGX7dMLn98bfZ1RAl7HVy08VLJSQJIcQMJkFJDJuiKCxZsiRzfUhSIy+nPGKxph7lwP9d1jya7lwBCB05adQ0LVKGiWHa6/0oil2rQ1WUzkv/v8eWZVe4m2oFF7Jhw75mvvePHbTF07gdKp8+Yx5nLy4aszk6uT4XV540m/ccX8n67fX8ZdMh9jXH+Ovmw/xt82EWlQTJ9bsIepyEPA5CHichr4Ogx0mw67bHic+tjXhNJ8uy+OG6nby6vxW3Q+XrFy2hLEfeM4QQYiaToCSGTVVV5s2bN7wnpScgKP3rp6AnoGgxzD9n/F9fjIu0aaFbKil3OalIirRhZi4p3cLob5GWPnqGp66vQNYq3E0VacPkly/uzZR0rirw86XzF1KZ28+aY2PA5VA5d0kJ5ywu5vUDbfx500E27mvhrdqhzW9UFQh6nIS9TkrDHkrDXspyPJl1nfL8rgGD1G/+tZ9/vl2fWVA2W8MLhRBCTF0SlMT4GO+g1LANtj9uXx9KOXAxpbTE0rTF0+iGiYVCKlSF2ZYGRrZwq2XZFzMzPHTm9CIZpsUbh9p4cVcTL+5uojlqL4B60TGlfPSUqglZHFBRFFZW5rCyMoeDLXF2NUToSKRpT+i0J9K0x/XO22k6EjodCZ142l64tC1u/27s72dRYJdDpSxsB6eyHiFqV0OU379SA8Bn3zGfE+Zkb/imEEKIqUuCkhg2y7Joa7PnLYTD4aMPxzENu2dnvFgWvPAD+/r8c6B46fi9thhTKcOkvj1JPG1k7tN9JZiuwCDPEn2ldJNNNa28uLuRl/c09yp7HvI4+PzZ1ZxUlT/IHsZPea6X8tyjD4FLGybtcTtMtURTHG6Lc6ite02nuvYEKd1kb1Mss1BuX+8/oZLzl5Zk+1sQQggxRUlQEsNmmibPPvssMMSqd+Pdm7R7HdS9Ya+lc+K/j+9rizHTFtdp6Ej06usxXSF0X+GEtWkqiacMNu5v4cVdjbyyt6VX2Ax6HJxclc8p8/JZUZmDU5t6PbBOTc0s4lpV4Adyez2uGyb1HUkOtcY51BbnUGsis75TYyTJ+UtLuPLEWRPTeCFmGFUFBWVIQ6OFmEgSlMTYG89CDnoSXv6JfX3FByBQNH6vLcZE2rSob08SS/Ve7NXS3KQCsqBlX4Zpdc7PMkmkDbYcbOPF3U28ur+FtNF9UpLvd7F6bj6r5+WztCw87dcJcmiqPeSunwINlmXJYrJCjDFNVQh5HYS8ToJuB5YFLbEUjZEUKX1mzQcVU4cEJTH2xrNHafPvIVIH/iJYccX4ve5Uorns4ZCWcfRtJ1hHQqe+I0HfDx0t1UUqODuri8pOZind5LmdDTy3s5FYyiCl28UqkrqZCUVpo7u630BKwx5OmZfP6rkFVBcHRlwhbrqRkCTE2HA6FEKdBVb8fdZFUxTID7jJ87toj+s0RBLEUxKYxOQiQUmMvfEKStEG2PRb+/pJn7KH3okjhSvtgNG0a9KGJd20aOhIEknqfR5RMDz5pP0lk6pAx/a6Du54cjtpw2TNgkLOXFSUlUpxDR1JHnvjMH/fWkt7ou/P4ug0VaEy18sp8wpYPTef2fk+CQVCiDHldqqEvXbJfq/r6B9mKYpC2Ock7HMSSeo0diR7zZsUYiJJUBJjy9DBSI3Pa718j100ongZzDtrfF5zqnGHwNO5CnX+fGjaOenCUkdSp6E9idFnlVdL85IKVmA5Js/aNpZl8ZdNh7jvxb2Znpw/bjzAHzceoLoowFmLiji9upCw1zmsfb5xsI2/bjnMS7ubMr1pBQE3FywtpiLXh1NTcTlUnJqC26Hh1BRcDhWXpvZ4TJ32w+mEENmnKPa8xYF6nHu+NVs9Zo16XRohjxOPc+Q9/QG3g4DbQSJt0NCRpC2enpELfovJQ4KSGFvp6Pi8zvbHYeeT9vXV19jv9KIPBULlpHQT07LwuHx2WGreBebEf3pnmNAQSfTzSaKK7itG9xZMquPaFk/zvX9sZ8O+FgBOnZfPKfMKeHp7PRv3tbCjPsKO+gg/e24Pq2bncubCIk6Ykzdgue1E2mDdtnr+tvkw+3qUtj6mPMw7jynlpKp8CT5CiDGV43NSGHSPKuxkg8epUZnno8QwaYwkaY6mMGVUnpgAEpTE2BqPQg51W+GZ/7OvH3cVFC0a+9ecivyFWA43+xuixFMGPrdGns9FOHceasvEhqWOpE5jJIlu9P7o0HSFSPvLsDTXBLWsf1sPtfE/f99GUzSFU1P45OlzuWBpCYqicMaCQlpjKZ7Z0ci6t+vZ2RDh5T3NvLynGb9b47T5hZy1qIjFJUEUReFQa5xHtxzmH2/VEU3ZvXtuh8pZi4p45/JSZuf7J/i7FUJMdyGvg+KQZ8IDUl9OTaU07KUo6KE1liKaNIilddK6dDOJ8aFY1vTu1GxvbyccDtPW1kYoFJro5kwLpmmyY8cOAKqrq1HVQeaKNO2CZPvYNSZSDw99GuLNMOcMOPemSTV3ZdJQHVC0hLpImvr2ZO+HVMhxGRQkanCr4/uRXTxt0BhJkUj3Gf6nOEkFyjDd4XFtz9EYpsUDG2v47b/2Y1pQnuPlKxcspKpg4HWc9jfHWPd2PU9vr6cx0j0MtSTkoSTs4fWa1szgldKwh3cuL+XsxcUE3PI5lhATyeNU8bkdODUFLDAte6iZZYFpWZkhYV3XTcseiGaadmGVbPeAKIrdJpemkTZN4ilj1MPSAh4HxSE3PtfUer9J6fb3H03pxFIGifTofxYjoapkevoVFBQFFLoGP/S8rWTu100L3bDQzez/joihiXS0c8qSWUPKBhKUxNiq3TJ2PRV6Eh7+HDRuh7x5cOkPwDn6CfTTUngWUUeY3Q0DD4VU9ASh2F5CLoWgx8lYLqWTMkyaIql+ijWA4Skg7SuedBXtWqIp/u/Jbbx+wF5s+ayFRXx6zbwhTVYG+yRqy8E21r1dzwu7mnqtY7Rqdi7vPKaU42blSiU6ISaIx6nidzvwuxz43RqOUb4J6oZJqrMqZUq3q1R23e7be95FUexeFLfDnmvodqi4nRruznmHPRmmRSSp25eEPqwS2z63RknIc0QluqnKNC3iaTs4xVMG0aQxpms0KQoUhzwUBt2j2o9pWnZwMu3KpYZpoRsmadPCMCzSpl3ZVHrQsms4QWl6/IWIyUlPjV1IsixY/x07JHnCcP4tEpIG4vRhePOoqe8YdDPL4aHdN4dk2x4aIxECHgfhIVYtGirdtGiJpuwJun1fX/OSCpRjTcLj+Nr+Fu54cjut8TRuh8pn1szj7MXFw9qHqiisqMhhRUUOn15j8NLuJhoiSU6dV9Dv2j5CiLGVCUZuB37X6INRXw5NxaGp+PoZOdzV65QyTNK6iaMzHLkd6pArU2qqQtjrzBSLSeoGkYSeCU/99VZ4XRrFITdBz9ALzEwFqqpkjmWXroIQrbF0Vl/L79Yoz/Xidoz+f6OqKrhUBReD/+51/b4kO0N3V+BO6oaEqDEmQUkMm2VZRCIRAAKBwMBv6mNZyOH138Kuf4KiwTk3Q7B07F5rqguVc7AlPqQ3U8vhIZkzF1fbHjoSKToSOi6HSsDtwONU8TgcI+ppMk1ojadoiaWOWBNpshZrAPsT29+8vI8HNh7AAubk+/jy+YuozBtdmPM4Nd6xcHoshux3a+imRTItY0jE5KapdhnqwBgFo+FQVQWPqmV1TpDboeEOaOQH3FiWRSxlEEnqnQVyLIpCHkLTLCANpqsgREHA4HBbnGhydBVeFcUeHp0fGF0v0kgM9vtiWVav3sqkLiEqmyQoiWEzTZOnn34agLVr16JpA7zRj1Uhh30vwL9+Zl8/9VooWzk2rzMdeHJo0V20xeNDfoqluUmFq3C17UEx7RXTm3V7bo2CXWjA47TfsD0uDecgldgsC9oTOs3RJHo/wyBMZ4B0oBxLG5t/PEndYOvBdjbub2FfUxSfyy49G/A4CHZ99Ti7r3d+9To1GiMp/veJbbx52J5jd8HSEj5xelVWPkWcDjRVoTzXm/k02+gc+hLvnC8QTxsSnsSEU1UyvS4Bt2PGrCOmKN09LMUzfNaB16UxtzBAeyJNXVuCxAjelwIeB+U53gGrlk4kRVEy/5P76gpRPcNTV4+UhKihkaAkxk566CfnQ9a8B576JmDBkstgySXZf43pQlFJ+ks42Dj842CHpbm42najmN0FCCwgoZskdBPi9nAGh6bgdWp4HBpelz2WHiCS0mmOpEj2N25e0Uj7SjC8+SP61gZst2VxoCXOxv0tvLa/hTcOtpMyhv9PUVPtibe6aeFzaVxz5nxOry7MalunsqDHQXmut9ecCU1VMmugdDFMi0TayEy2nqnhSVMVuyAAZIacWj0KA/S83XW9S9d5fWaieN/bPbZL6mMzOdzlUAl4HLg0lbr2xKRf10ZVIeSxFzANzqBwJAYX6vxQrCWWpq49MeA8sZ5UFUrDXvL8k6vy6lANFqK6hvMl0gaNkSTx1Mx7bx4KCUpi7GR76F2iDf7+NTuAla6EU67J7v6nGctfSE2bPuKTGktzdYalPShmcsDtdMOiw9DpwJ6PpnZORu43IAGmM0QqWA5qdoaARJM6rx9o5dV9LWzc30pjpHdbCwJujpuVw+KSEGnTpCNhD0WJJNOdX+2J0B1JnY5EOjOhFmB+UYAvn7+Q0rDMIQL7ZLwsZ+gnDVo/8wYM06IjkaYlliaaHPnv52SjKGQm39tftcyck/Ec4pVIG0STOtGkPbF9KCeDfSkK3T2vHkevXlS/W2NvY2xMJ8qPhKJ0hiOv014sVdYcE/1QFIU8v4scr5OGSJKGjuSA70H9fSA0nfQczpfjc9GRSNPQkRz1EMXpRoKSGBvpBFhZ/HTC1OEfN0PHIXs+0rk32SWvRf80F3VmDvHU6Cax2mGpCmfkEGq6A44owXAk02KAXiQHaX8ZhidnVG0CONgS57ldjby6r4W3a9t7zXtyagrLysIcNzuX42blUpnrHdYnyl0TohNpk9Icj1Sh6+Rza1RkYQKzpirk+Fzk+FykDZO2eJrWWGpKfZrp0JRMgMhUJhvGJPyx1PXpcX5nxfqkbhBL2nNVYiljwMpoHqeaGYrqd2kDfi8+l4N5RX72NcUmvHewK9Dl+JyEPE4JR2LIVFWhOOQhz++irj1BayydCUyaqlCW4yGnvyoc01jQ4yTocRJL6dS3J/tZ/H1mkjNNMTbSWZ6f9OJdcOhVcHjg/G9BFk62p7OIu4iGSHYq/dhhaQ5YJmqqA63zgjX0/RvuHNL+slGHW8O0eODVA9z/r/295jxV5Ho5bpYdjJaWhUY1QbprQrSwKQoUhdwUBT1Z37dTUykIuCkIuEmkDdriaVpiqUk7dl5RoDDopjDgnjIn5XbPlkZuZy9g2jDt4JTSMU0r03M0nE/N3Q6NeYUB9jVFJ+TTZ4dm9wrk+lyTcs6ImDqcmkpFrl3wobYtgaoolOZ4pm0v0lD4XA7mFDgyVQPb4ulp0/M/EhKUxNhIZXHY3dt/ha1/sq+f9TXIm5u9fU8WimaXOffm2GGi7cCIw6au+aiJuxlK78+wKCqmO4zpDpMGFD2OlmpHS7ajGP3Pg7JUF+lAGaZr9LOJa1pifPfJ7eyotysurqgIc9r8Qo6blUNRKPsn8cLuZajM82W1MtfAr2X3hBSHPESTOq3xNG2x9KQZ4pXjc1ISnvonUE5NJexTCftGN/RVUxWqCvwcbI3TEs1u+eWBBDwO8vwuQh6ZdySyy+PUmFPgn+hmTCpdVQOL9O4y6zMxMElQEmMjW4UcajfDc9+zr6/6GMw5PTv7nQwUFdwhOxy5w/as0S4FCyDaAB2Hhz2E8ZCVN6J5CcNlObzoDi+6rxjMdGdPUztqKgKYGJ580r6SUS8ca1oWD79+iF+9uI+UYeJ3aXxqzTzesaBQTpbGUGHQTXHIPSE/4655TWVhD+0JnbZYmvbExPyT9rs1SsPerK4nNl0oikJFrg+XI0Fd28DzGEdDUxVy/U7y/C6pOCnEBHA7NCpyfRSHTBojSZoiqRkVmCQoiWFTFIV58+Zlrh/BsrIz9C5SB0983Z6fNPcdcOyHRr/PCaeAOwjeXLsHaaAQoSgQKLKHGLbVQLJ9SHtvsYK06RMwrlp1YnjyMDx5YFkoZiorJb9r2xJ875/b2XrI/v6Pm5XD586qpmAC1rGYKZwOhcpcX68CDBNFUboX1DRMi/Z4mtb4+BSB8DhVisMza92ZkSoKenBrGjUtsawdF59bI9/vIux1ygciQkwCTk2lNOylMOBmb1N0Ss0rHY2J/08ophxVVVmyZMnAG6RjjHrYV7LDrnCXaIX8aljzlUm3GOmwuIJ2z5EnB7Rh/Nk5XJA/D+It0HYQzIGHuCQNhYPkdtcLniiKMuqQZFkWj2+t5f89v4dE2sTr1Pj4aVWct6RYTprGiKraFQIn6/wbu2fBRa7fhW6YtMbTtMbSxFPZnSPj0OxJ3rk+OUEfjrDPidPhH1VFPKdDIeSxe4/GY7inEGL4HJrKnHw/uxujY1bQJdfvHNEw58xyCz2WWuhekqF7OQZNH/oHYBKURPaNdthdaw38/Qa7J8WbC+ffAs4pWp7Z6Ye8KtBG+am0N9ceptd+EGJNRzxsWXDICGE5pv6fdENHkh88tYPXaloBWFYW4tpzFlAi85DGhKJAfsBFYcA9rmWsR8PRowhESjdpjadoi6VHtJBkl6lYqGGyGW5FPIdmr71lD7XUZGidEFOEQ1OpKvCzuyE6YCXNkRjuEhQj1e4YekW/qX9WJcadZVnE43YY8nr7Kb08mkIOB16xy4CnIuAvhPNvhUDxKFo7gRQVcmaNPiR1UTV7f948O0TqicxDjQmIuPKy8zoTxLIs1m2r555ndhNNGbg0lQ+vns3FK8qkRPcYUBS7QEFxaGoXKHA5VIqCHoqCHhJpg9ZYmkiyq+fVXpRVVewFhLuu0+O6ooCm2L1VU/nnMFkMVhGva1Fiv1vD73ZIr5EQU5izKyw1RrJSqVRRoDLXN+pCM9kmQUkMm2ma/POf/wRg7dq1aFqff3Yj6VGyLHjjQXjpLrt4QfFSOPcb4MvPQosnSLAMnGPQC+IOQOEiiNRhtB8mmtCpV0qGNDTRsixeP9BGNKmzqCRI/iSZ69MSS/GjdTt5eU8zAAuLg1x3TjUVub4Jbtn0lONzUhRyT7tP8D1OjZKwBkjv40Tqqoh3qC2Bbpj43Q4CEoyEmHZcju6epdEUkVIUmJ3vIzgJ54RKUBLZZZqgDzMoGWl47ruw7VH79oLz4fT/BG0KL/bmCkKgMKu7TOkm8bRBMm0QTxsk0n7S6TI0vRXTf/Ty22nD5K6nd/KPt+oz9xUEXCwsDrKwJMjCkhDzCv3jdvJsWhZbD7Xz7I4GntnRQDRp4FAVrjxxFu8+rgJNhj9lXcjroDjkkRNWMeYURaE8Z4oOmRZCDJnboWXC0kjmJ6oqzMn3T4oCQv2ZnK0SU1d6mMPu4i3w5Nehdos9VO2kT8Py907twg1K5xC5EbIsi0TaJJEJRPZXs79hwA4PuqPkqPvsSKS59dG3eONQO6oCs/J87G+O0RhJ0Rhp4vld9rynrk+CF2XCU5CSkCdrk9oty2JHfYRndzTw7I5GmqKpzGNVBX7+45wFVMlaFlnnd2uUhD34XPKWL4QQIrs8Ti0zDK/fc5UBaKrC3EL/pP7wTv5riuwazrC7pp12ZbtInV304Oyvw6yTxq5t4yVcYVer68GyLNKGhWFapE0Tw7DQTQvdNNE7rxummdkmm6WPD7XGufmRrRxqS+B1anzlgkUcPzuXeMpgZ0OEbbUdbKtr5+3aDlpjaXbWR9hZH+GvWw4DEPI4qC4OMivPl7lU5vqGta7M/uYYz2y3e44Ot3XPrfK7NFbPy+f06kJWVORIL9IguubUqCoombk30N88nK7HFQXCXuekHM4ghBBi+vC6unuWhnIO43QozMmf3CEJJCiJbBtqIYc9z8K6b9kFCULldtGG3Nlj27bx4AmDzy6qEE3qNEaSRJL6sD5hyaYtB9u47dG36EjqFAbd3HjREmbn2z02XpfG8vIwy8vDgB3mGjqSbKvr4O3aDrbVdrCrIUJ7QmfjvhY27mvpte+ioJvKrvCU62NWfu8AVdue4NnOcLS3qXtdLZdD5aSqPM6oLuT42blTegJ9z6pdPpeGQ1UwLXtYoWFaGJaFafa8TuY+07IwLXCoCqqq2F8VBU3tvnTd17WNEEIIMVn5XA7mFPjZ2zh4WHI77RLjLsfk//8vQUlk19EWmrUseO1XsOH/2bfLj4dzbrIXYZ3qVCeEZ9EWT9MYSRJLZnd9l+H6x1t1/GjdTnTTYmFxkK+tXUzuICU3FUWhKOShKOTh9Gp7flXaMNnVEGFPY5T9TTH2t8TY3xyjNZamviNJfUfyiABVGHQTcDvY09gdmh2qwnGzcjljQSEnzskbVm9UtrgcKrppjiq0Oh0KfpeUMxZCCCH6E3A7mJ3vY19T/wtQe112SJoyy1FMdAPENGLoYKQGflxPwNO3w+519u1ll8PJnwF16v8aWha0OotpaIyP2QJsQ2VaFr9+aR9/3HgAgNPmF3DdOdUjOql3aiqLSkIsKuldLKI9nqamMzR1XWqaY7TE0jR0JGnoSKIqsLw8zBkLClk9N39Chn91DT0rCLgz4czsHP6oGxa6YZEyzMwQyLRhD39MGyaWZYcrn0vL9BpNhU+/hBBCiIkU9DipzPNR09w7LPncGnPy/VNqmP3UP0MV405RFObMmZO5njFYIYdYEzz+VWjcbgejU6+DxReNaTuHxOEdfpW+HgwT2hIpms0AccsFTGxISqQNvveP7ZniDFesquTKk2ZlfR2ikNfJUm+YpWXhXvd3BajmaIplZeFBe7DGkqYq5Pld5AeOXBtHVRXcqsbRCuyYpiXD3YQQQogRCHudWLleaprtc6yAx8HsPN+U+78qQUkMm6qqLF++/MgHBivksOE+OyR5wnDuN6H0mDFr35C5Q5A/D6JN0H7AXr9piNKGRWs8RVssjam6SOaUjmFDh6YlmuKbf3uTHfURHKrC586az1mLxnex3q4ANVHcTpV8v4tcn2vUb8ZT7c1cCCGEmExyfC5MCyIJnco8b9Yq6I4nCUoiewYr5HD4Nfvrmq9MjpCkaBCutK/78+1FXFv2HbW8eVI3aYmliCR0unqT04EKUCd2rsqexijf+OubNEaSBN0Obli7mGXlExdYxpvfrVEQdBOS6m5CCCHEpJHnd5E3QaNLskGCkhiRVMqei+Ry9fjlH6iQQ6wZ2g4ACpT00xM1EULlvUt4O9xQUG2XKu+oBXrPQDRNaI6laI2lej2iewsxXYFxafJANuxt5jt/30Y8bVCe4+XrFy2hbAYs9Ng1/6gw6J705UWFEEIIMfVIUBLDZhgGf//73wFYu3YtmqaBngJT7/8JdW/YX/OqJkd1O3fI7kXqS1EgWGI/3rrPLj4BRFI6DR1JdKN3eLI0N7pvfIe2dUmkDV7Z28z67Q28srcZ04JjysNcf+GiGbFmTo7PSUnYM6VLiwshhBBicpOgJLJjsCFrtVvsr5OhN0nRIGfW4Nu4fFCwkHRrDY11h4gk+wuACqngLFDG70RdN0w2HWhl/fYGXt7dTDzdXX783CXFfGbNvGkfHDRVoTzXS9g7/cOgEEIIISaWBCWRHYMVcphMQSlcAdrRT7IbY2nqEjngVnGlDoKV7vW47ivCcoz98DbTsnjrcDvrtzfw/M5G2hPdoa0o6GbNgkLOqC5kToF/zNsy0UJeB+U53imz9oIQQgghpjYJSn1ZFiRawZs70S2ZWlIDzE9Kx+1qdzDxQckdAl/eoJvEUwYHW2PEU50V8FwhErk+XJEDqKl2ACyHD91bNOh+TMti/fYGfvev/TRHUxQE3OQHXOT7XfZ1v4v8gDtzPexzZkp4W5bF3qYo67c38MyORho6kpn9hr1OTp9fwJoFhSwsCU7JCjLDpapQnuMlxzd1J4MKIYQQYuqRoNRXrAnaD4LT33uyvxjcQIUc6t+yy277iyAwtvN5LAvSpolTVTkiPxxlyJ1hWtS1J2iOpo5cSVp1kArNQUs044zWkgpUcOQLdHv9QCv3Pr+HXQ3dwxEPtsY52Dpwr1vXuj8FfheRpE5NS/e2XqfG6nn5rFlQyIqKnCm1UNtoBT0OynO9035IoRBCCCEmHwlKPRk6dBy2T+zbD9rFB8TRpRNgGf0/Nk7D7iwLatsTRJI6CqBpCk5VxaEpuDQVLW82Dl3BhXnESXdbPM2h1vgRxRr6Mjx5GO6cAecl7WuKct8Le9mwrwWwA857j69g9bx8WqIpmqIpGiMpmiLJzutJmiIpWmIpDNOioSOZ6T1yqAonzMljzYJCVs3Jxe2YWVXdVBVKw94pXVJUCCGEEFObBKWeOg53V25LtEKyY3JUaZvsBupNgnEJSqZph6Royj52FqAbFrphQBpMZ4hU3Atxu4dHUcDlUHFpKqZlEU0OEPL6009Iao6m+O3L+3jyrTpMy+4dunBpCe8/cVam6EBFrm/AXeqGSUssTVPUDk6mZXHsrFwC7pn55+l3a1Tk+nA5pBdJCCGEEBNnZp6J9Scdt4fd9dR2EAoXDjrMalpIJ+x1jnx5R53DA6AoCpWVlZnrAwYlU4f6rfb1MQpKpgmH2uK9KsD1omikguW97rIsSKZNkmlzVK8dTxk89NoBHtp0kETnvlbPzeeq1XMozx16oQeHplIYdFMYdI+qPVOdokBJ2ENBYGb/HIQQQggxOUhQ6tJ2gL6LjKLHIdoIgcIJadKYsyyINnQPN0x12OEmMHihAlVVWblyZfcdAxVyaN5tB1CXH3LnZK3ZXYzOkJQYKCQBaX8ZqNktJW2YFv94q47fvLyPlphdDW9hcZCPnVbFktJQVl8r2xQF/G4HDlXBtCxMyy48YZrd1w3TOnKe1hhRVXA7NNwOlaKQe8YNMRRCCCHE5CVBCSDeAqlI/491HLYr4GnT7EeVjkNrzZHrH7UfBNOAUOnQ9mNZA/codQ27K14GanZPgHXT4nBrnIQ+cK+Q6QpheLJXvdCyLDbsa+HeF/ZS02x/zyUhD1edModT5+VP2gp0mqoQ9DgIeZwEPQ7UIRaDsMOThWFZmKZdKEM3LNKGSdqwr+umSdqwBp3fpSjgcaq4NA2XQ8XtUDNfpdS3EEIIISaraXb2PwKmCe2HBn7cMqDj0NEXKZ0qLAsiddBRyxE9aF0itfb3Ha4YcDeGYffiaEZy4P2M0fykdGdISg4SklAcpALlAz8+DC2xFE+9Xc8TW2s51JYAIOh28P4TK7lwWemkrMjmdqqZcORzaSMKcaqqoKJk3iS8DBx2LctCN+3AlDJMTNPC2RmGJuPPRwghhBDiaCQoRerASA2+TawJfPn2ELKpLB2H1v2DF1/oEm2we5ZyZh0xR8swDB599FEA1q45sf/TZ8sak6CUNiwOtsZJG4PPLxrtkDvDtHi9ppW/v1nLy3uaMUw7DHqdGhcuK+G9qyonVbEFRQGvS8v0Gnmc4zuETVEUnJqCUxs8UAkhhBBCTBWT50xvIuhJiNYPbdu2g1C4YGzbM1Ysy+5BitQxYO9Pf+LNds9SbtXABS3SA6wN1HHYDpiqAwoXDbvJ/UkZJgeHUMbbHnKXM6LXaIwkefLNOv7xVh31PRZ6XVgc5NwlxZxRXYjXNXmCgNOhUBT0EPI4ZBibEEIIIUQWzeyg1H7QLmIwFOkoxJqHVBVuUknF7F4kfeDFTgeVaIOmXZA3155531ffOU5dunqTCheCY/RVzJK6aa91ZA4cklKGRUNcxVFYhN8whxwcdMPklX0tPLG1llf3t9D1En63xpkLizhvSQlVBZOrN9HpUCgMuMnzuybt3CghhBBCiKls5galRLsdAoaj/SB4wlkvTDAmLMvu1YnUM6xepP6kOqBppx2Weha1sEy7V66/4gCZQg6jH3aXSNshyRikFNvrdTq3vBCnNWkBrwL2MDm/W8PvchDwOPC7HPjdGgG3A3/npTWW5qm36zLV6wCWlYU4f2kJq+flT7oqbA5NoTDoJl8CkhBCCCHEmJqZQcmy7NAzXKZuh49BihxMCqloZy9SInv7TEftsJQ/D7B7ajQzhR3CBglKo5yfFE8ZHGqLM1BHkmVZPLQ9xU82JTEtcKhKptcpnjaIpw0aOcocNCDH6+TsxUWcu7hkWGsgDcblUCnP9eLSVNoTaToSOtGkPqLS25qqUBB0UeB3D7lqnRBCCCGEGLmZGZSijSMPEdFGu7CDMzsn01llmnaQG+q8q+HS49C4A3LmAKCZyf63S7RC6z77esmyEb2UaUJHUqehIzFgf1hCt/jeKwn+uc/uDXrHwkKuOXM+DlUlmtSJpnSiSYNI0g4oPb/a1+3KfafMy+fEqrysVWdTFCgIuCkKdoeagoCbgoAbw7SIJPRMcDIGGUoI9mjHwoCb/IAbTQKSEEIIIcS4mXlByejsFRoxyy7sUDA/a03KimTE7kUyBggv2WIkoWknqpkaOCjVbrW/5s6xhyoOQzxl0JZIE03qA/YiAdRGTG56LsauVhNVgY+fNpeLjynNDEcLeZ2EvNldaHYovC6V8hzfgAUfNFUh7HMS9jmxLItYysiEpmS6e76cokBh0A5XEpCEEEIIIcbfzAtKHYfsSm6jkeqwF6n1Zm8x0xEzTft7ijaM20sqps6cQBqUAMpgw+6GOD8pbVh0JNK0J/Sjlv0GeLVW51svxGlPWYQ9Dr5y4WKWlw8vkGWbokBRyE1hwD3kuUOKomTmSpWGIakbtMftXqaCgEuq2AkhhBBCTKCZFZRSMbtkdTa0HwJ3uP9KcOMl2QGtNWPfi9SHqiosXbxw4A3qjj4/yTQhkrSHoMXTQwuulmXxwLYUP3vdno9UXejjq2uXUhgcfVW90fC7NcpzvaMu/OB2aBQGJ1fxCCGEEEKImWpmBaWRFHAYiJGy1yUKlWZvn0NlGvb3kq3Ql016Ehq22df7CUrxzqFmkaMMrTviebrFHf+K8/R+HYBzFhXymXdU43JMXFBVVSgNe8nzuyasDUIIIYQQYmzMnKAUawYzkt19RuvtdZWysE7QkCXaoa3GDmqTUf1bdnVAXwEESzJ3WxYcaI2TGGLvUU+HIyY3PhtjT5uJpsAnT69i7fKyCS2PHfI6KMvxZq0AhBBCCCGEmFxmTlDqqAV/lgONZdo9O3lzs7vf/pgGtB2AePPYv9ZRGKbJ8889D8Cpp52K1nP4Yc+y4D2CTEMkOaKQ9MphndtejNGRglyPylcuXMrSCZyP5HQolIa8hH3jXyhCCCGEEEKMn5kTlMw0MAY9P4k2u5fHE8r+vgGMtD3ELtrY+T1Mcv3MT+pI6LTFh9f2lGHxwNsp7tuSxAIWFbq5/p3HkB8Yn947h6bgdqh4nFqvr1JgQQghhBBiZpg5QWkstewFbw54csAd7NWTMmLJiF3JLtEGA64kNMmYRndp8M6glNRN6tqHvmbVnlaDR3en+efeFB2dowsvXBjik2ctG9Iwt4Fqa/Rbna9ze7dDOyIUSUluIYQQQoiZTYJSNliG3esTawLVYQcmby64A8Pbj2nYZcejjfbirlNNyx5IR8Hpg7wqTBPq2gdeMLZLXLdYvz/NY7vSvNnUPTyvyKdw5fElnL1i3lFf2ulQKM/xEvTIkDghhBBCCDF6EpSyzdQh1mhfNFdnaMoBl3/g56TjdjiKN9vznqaqzPpJS0F1UN+eIKkP/P3saDZ4dHeKp/aliXWOzNMUWF3uYO08D8uqq1CGsGBtfsBFSciDKr1AQgghhBAiSyQojSUjZVfGi9aD5u4enufy2WXg4i12L1Qqy9X4JkqPQg5tcZ2OhH7EJtG0xVP70jy2K8WOlu4QVRZQuHCui/OqnOQGfKRCs7G0wecjeZwq5blefC75NRZCCCGEENklZ5jjxUja6y5F6sDhsXuezCODxJRlWVC7GYBU4VIaOnrPS2qMm/xic5Kn96dJdI6uc6pwaoWDtfNcrCjSUBUFw51DMlABysDzkRQFikJuCgPuCS0RLoQQQgghpi8JShNBH3pxg8lIQSE3LzdzHbADYLQRS9E47KrqNS8poVt8bX2M3a12D1JlSGXtXCfnVjkJu9XMXtP+UgxvwaCv7XdrlOd6cTu0LH9XQgghhBBCdJOgJIZNVRWWL1vW+87OYXfp3PmkFFfmbsuy+P6GBLtbTXLcCl8/1cuyQq13T5DiJBWahekceB6XqkJp2Eue3zXgNkIIIYQQQmSLBCWRHZ1BKZq7uNfdf9uV5h9706gK/NcpXpYX9f6VMx1+UqFZoA5crS7sdVKa4xlSeXAhhBBCCCGyQYKSyArz8BZUIJ6/JHPftiaDu161hxl+7Bg3K4p7/7oZnnzS/rIB151yOhRKw17CXin5LYQQQgghxpcEpU6WBYmUjr79CdT6N0iv+BDenGLcDunF6MswTV588UUAVq9ejZWM4GjdA0CiMyi1J02+8XyMtAmnljt436KeQ+ZU0oFyDE9uv/sPeBzk+V2EPA4p1iCEEEIIISbEjA5KumkRSxpEUzp6ww4KXv8xwaY3AUjVvsbB024DfyFel4bf5cDj0nBOo7V6krpJMm2S0A2SaRNNVfC6NHwu7agB0TS6S3u37nmNAiAVKMdw52CYFre9GKc+ZlEWUPnSSd5M4LFUN6nQLCyHt9f+VBXy/C7y/C4p1CCEEEIIISbcjAtK8bRBLGUQSxokdAM1HSPv7d+Qs/sRFMvE1NyYTj+u6GHKn72eg6fdSoevKLMmkNuhZsKEz+kYaNTYpJM2LBJpIxOKkrqBaR25XTRlf5+aYocmr1PD59ZwDTA/qDmSQquz5yfF8+zepN9sTbKh1sCtwY2nefG77B+S6QyQCs4CtfvXzutSyfO7yfE6ZcFYIYQQQggxacyYoFTXkaQxoWBYnenAsggcfIbCN36OI9EMQEfZKTQu+wQA5c/dgCtWS8VzX+XAabeh+4qAzl4Y3aQ1lkYBvC4HPpeGQ1VwqAqapuBQVNQJHLFnmJDQdbu3qDMU6f2losH2YVlEkjqRpA4RcGiKHZqcDlydHT5JA1piKSqb7V64RP4S/nUoza+3pgC4dpWHuTndvUO6twhUO1yGvU7yAy5ZLFYIIYQQQkxKM+YsNZLQCTjssODsqKHo9R/ja+xcINVfSsMxnyZWfHxm+4Onf5vy576KK3qYimev58Bpt6L7S3rt0wJiKZ1Y6siFYxXAoalonQGqZ4jSNHCoKk519IHKNCGpGyR0OxAl0ibpHsPihsLVtoecPX8jGZ5LW9XafrfRDYsOQ6cjoWOaJg0JMIHZRgp3y3YA9nsX8+3nEljARfOdnFvVe16S5g1QGHCT53PhkAp2QgghhBBiEpsxQQlA0RPkbbuf3J1/RrF0TNVFy4L30lJ9OZbWe30e3VvAgdNuo+L5r+GKHOzuWeoTlgZiAWnDJG0Mvp2mKDg1BYem4tAUnKqKw2F/7RukLAtSht1LlEjrpDp7t4bXV9TN3bqTvLfvJ1D7UuY+R7yBpsUfHrASXRej80U9rTtRTR3dlcMNr4bpSFkszFP5zLGeXtur3gBVRUEJSEIIIYQQYkqYMUEpXP8vKnf8Eme8AYBo8QnUH/OpQYOP0RWWnrsBV+QAFc9dz8FTbyUdKMtauwzLwtAt0PvvBeoKUigKybQx4lDUk6f5bfK23Y+/bgMAFgrx/KX4mt4gb/sfUfUEDcs/CcrRQ423+S0AtqgL2dFqEXIpfP1UHy6tO2gpQGlRkYQkIYQQQggxZUyJM9e77rqLqqoqPB4Pxx9/PM8+++ywnv/XD3iZu/n/cMYbSHuLOHTSf3Ho5K8PqXfI8ORx4LTbSAYrccYbKX/uqzgjB0f6rQybYVkkdJNEFkKSp/ENyp7/byqf+SL+ug1YqGzLXcM3i77LRdH/4h7fJ7BQyNn9CMWv3QnWwN1hgUCAQCCAt3N+0l87qlGA61d7KfL3/rUqDHrwBXJG2XohhBBCCCHGj2JZVjY6KcbM73//ez70oQ9x1113ceqpp/KTn/yEn/3sZ7z55pvMmjXrqM9vb28ndEclpqLRWn05zQveh+XwHPV5fWmJFsqf/xrujv3onjwOnHor6WDFSL6l8WVZeBs3k/f27/A1vQGAjsbfOJ07kpewz+odFi9Tn+N/nXfjUEx255xK/JT/xOVy9bdnsExm/+1KXHqES5LfZOXSJXxombvXJiGPg+KcIJQsG5NvTwghhBBCiKFqb28nHA7T1tZGKBQadNtJH5ROOukkjjvuOH784x9n7lu8eDGXXXYZt91221Gf397ezstXl1L+wR/gLK4eVVu0ZCvlz/8X7va96O5cDpx2K+lg5aj2OVZ0w6R1z0Yqdt1PZXwbAClL44/GO/ixcQkHrEJUBRbkqiwrdLCkQGNvm8m6/WmWRF7mB847cSkGT5vHcn/Jf3LaHD/Hlzhw9CjhrTftZfGz1xCz3Hws915uXhNE7TG3yePQqMj1ovjyIHf2uP8MhBBCCCGE6GnaBKVUKoXP5+OPf/wj73rXuzL3X3vttWzatIn169cfdR9dP4yXn3qYQMB/xOOKouBwdE/VSqfTA+5LURRcRpSK57/WGZZy2HfSzSSDR/Zs9d2vrusM9KPOxrYJ3eLtZpM3Ggx8dRu4JPogx6i7AUhaTn5rnMW91kXk5hWytEBlWYHKojwVj6M72DidTizLYneryYG3XuaD9f+DhzQvGEv4RPqLaC4Pp5VrrKnUWFni5rl/Pswnoj/lXyzDPOcbhNzd+9IUhfIcD06HiqtwPoo/P/PzNYyBh/S53e7M4rTD2VbXdXT9yOqDI9nW5XKhdlbRyOa2TqcTTdOGva1hGIP+XjocjszvxHC2NU2TVCqV9W0tyyKZTGZlW03TcDqdWd9WVdVePaWJRGLCt00mk4P+3bvd7hFtm0qlMM2BK2F6PJ4J33akf/fyHiHvEfIeYZP3iNFvK+8RM+c9or29neLi4iEFpUldzKGxsRHDMCguLu51f3FxMbW1tf0+J5lM9vqBtbe3A/CTu+/G5XIesX3V3Lm9QtiPf/xj9AF+QSoqKnjfFVdw4LRbKX/+v/C07aZo3Rf53qGVHEoFerexpIR/+7d/y9y+7977aG9v63e/eXn5fOSjH8nc/vWvfk1zc1O/24ZCYT7xyU/QnrRD0R+eeYMDepAOZx7naq/yOcefWabuBRVilot13vPYWXEZVaUFnPvknzi0qYYW4NnOSxeH08nnP/95FEVhXq7G5ro6fnhoGZ8t3cIp2pv8mm/xkdT1PLbHz2N7DLxqnG9pb4EG7QR58Cc/6NVOZzqCatl/wN/+f4/Q9Xb4ox/9iH/+85/9fm8Av/71rwmHwwD87Gc/49FHHx1w25///OcUFdnrW/3yl7/koYceGnDbH/3oR5mhmn/4wx/43e9+N+C2d9xxB9XVdu/jww8/zL333jvgtrfeeivLly8H4O9//zt33333gNt+/etf54QTTgBg/fr1fO973xtw26985SucdtppALz44ovcfvvtA2573XXXcfbZZwPw6quv8o1vfGPAbT/96U/zzne+E4CtW7dyww03DLjtRz/6Ud797ncDsGvXLr7whS8MuO0HPvABrrzySgBqamq4+uqrB9z2Xe96Fx/72McAaGho4OMf//iA265du5bPfOYzgP23/MEPfnDAbc8++2yuu+46wH4feO973zvgtqeeeirXX3995vZg265atYobb7wxc/uDH/zggG/Ky5Yt69XT/fGPfzzzHtRXdXU1d9xxR+b2Zz/7Werr6/vdtrKykrvuuitz+z/+4z+oqanpd9uioiJ+/vOfZ25ff/317Nixo99tQ6EQv/nNbzK3b7zxRt54441+t3W73TzwwAOZ27fddhsbNmzod1uARx55JHP9jjvu4Pnnnx9w2z/+8Y+ZkyZ5j5D3CHmPsMl7RDd5j7DJe4RttO8RgwXBvqZEMQelT6lqy7KOuK/LbbfdRjgczlwqK4c3NC6puGlwV2AycHls0xXi4KnfoiYVIqilua5sE0t9TShZqUnXv7jm56B3Hhvcx/OJRyNc/lCEG5+L85ZaxemeXTzq/ho/cX2PZepeYqaTh9sWcGvd6Sy44NOsXVbC4nwHqjK89u1K5HDnoRVEDQfHabv4i/pllsU24jCTxE2NExR7SF+bmtfreZoRz4SklKmCdmRAFUIIIYQQYjKbdkPv+utRqqysHNLQu7eadG56NkZzAsoDCh9f7uSkUjUTyvoOezNiLcx++Wa8bTvt9vpKaJl1Lq0VZ2F6crMy9O7tJpOfv5Fma2PvLmkNg4/6X+QT/JkS45DdHoeP5jnvpLnqIgyX3ZXY1d14tDYMtq27fS+zX74JR6qNpL+cHcd/nSc2H+ALrd/EUlS2n/8bDM3+pCfgclAc7u7St3wFuAvnSpc50mU+km1lWI1NhtWMflt5j5D3CHmP6H9beY+Q94iZ9h4xnKF3kzoogV3M4fjjj+/VpbxkyRIuvfTSIRdzCIfDvPrsY/0GpS6P705x54YE6T5/pyuLND51rIf5uVq/z1NTEfLe/g2h/U+h6VEALMVBpGw1bXMuJF6w/KiLt/bnYIfJzzcneLbG/iNQFZifq7KiAC5Vn2VV/YN4YvbwQ8MZoHXepbTOvRjTFRhstyPmjByk/Pmv4Yw3kvIV87brGI5pfZJEeD41Z34PAJdDpTLH12uRXPLmgic8Jm0SQgghhBBiOKZNMQfoLg9+9913s3r1au655x5++tOfsnXrVmbPPnoltaMFJcO0+MmmJA9tt1PuqeUOrjnew192pHhwW4q0aS+Yel6Vk48c46bA2/9oRUVPEDz4LOG9j+Fp2Z65PxWooG3O+bTPOhvTNfjBAGhLmvz6jRSP7ExhWPZrn1/l5CNLVeY2/JPc7Q/gjNtjlHVXiNb576Kt6p2YTt9R990fj0PF49LwuTSSuklbPI1u9P8r4YjWUf7Cf+GKHs7c1zz3YpqO+RSqAhW5PtyOnj8fBUqOoXdyEkIIIYQQYmJMq6AE9oKz3/nOdzh8+DDLli3ju9/9LmecccaQnjtYUGpPmnzz+Tib6u1u2Q8tdfHBZe5Mieu6qMnPX0+wbr/dq+PR4H2L3bxnkQuvY+BeIlfrbsJ7HyN04GlUPQ6AqTqJlJ1GW9UFJPKWHNHLlEybPPZ2M+u3NxAw2iigjWPDEc4sjJCvtOGr24gzYRd40N05tFRfTtucC4e9JpRDU/A5NXwuB16X1qvcN4BlQSSp0xpLk9CP7K7WEs2d60nZk0QPrvoKsYrTKQl7CLr71AZxBaFg/rDaJ4QQQgghxFiZdkFpNAYKSntaDb7+bIzaqIXHAV852ctpFf0XHXirSefu15K82WgHh3yvwkeXuzm3ytlr3aC+lHSM4MFnCO95DE/brsz9yeAskjnz0JJtaIlW9FgrHr0NBwOPowVIe/JpqX4P7XPOw9Lcg27bRVMUvC4Nr1PD59ZwaUPv3YmnDdriaSIJvVeZCiXeSs7TN+DTWzl47t3k5hVQEOhnUdpgKQRLjrxfCCGEEEKICSBBqYf+gtKzNWm+83KchA6lfoWbT/dRldP/HKQulmXxTI3Oz15PUBu1f2Tzc1U+tdLDyuKjVFm3LNytOwjvfZzggfWoxsCTzpKaH7w5mO4cdHcORuclFSgjWnoK1lEqyCmApzMU+ZwOPM7RD3tLmxbt8TRtsTSGZWGaJju2bwcsVixbQmXeAHO/ChaAa+B5YUIIIYQQQownCUo99AxKPr+PX76R5Ddb7flIxxVrfO0UX6+FUo8mZVj8ZUeKX29NEussCrK63MHyQg1NAU1V0BRwqNi3FQVNtYsxaCp4jSgVzS+wq7aV1zuCNFoholqY06uLOGdRAU5XPz0zR9FznpHX4RizKUGmCR1JnbZ4iqRu4tAUKnN9RwzfA0DRoGRkhSyEEEIIIYQYCxKUeuj6YTz31KP88A2VFw/a840uX+jikyvcaP2d5A9Ba8LkV1uT/HVnGnOEP0GHCpdWu7hyiYuQe+jp5mjzjMZDLGWgqUqf4g09eMJ2xTshhBBCCCEmieEEpaOMGZseHDmlXP+8SU3ExKnCf5zg4dyq4ffc9JTjUfnc8V4ume/irzvTRNIWhmmhW3bPi2GBYVkYXddN0C27yp5hwbwcjQ8tc1MaGFpA8rsc+FzDn2c0VnyuwYcq4j56hT8hhBBCCCEmqxkRlEqu+i41EbsIw02n+ViUf5ST/GGYHda4+vjs7a8/IY+D4tDwqtuNJcM02bBhAwCrVq1C62+snzs4zq0SQgghhBAie2ZEUNI8ARbkwDfW+MkfYB2kycrj1CgKTp6Q1CWZGLggBZoLHEOryieEEEIIIcRkNLVSwwhFNj/BLavVKReSHJpCSdgz9eohSG+SEEIIIYSY4qZWchihpsfuxKlNrbShAKUhL84JKNQwahKUhBBCCCHEFDcjgpJtan2rxSFPVtZAmhAuCUpCCCGEEGJqm6Jn4sOXyK3G8BRwtG9ZUxSKgh4msiMn1+ci6Jmi08ecPtCmaNuFEEIIIYToNGOCEqqDdKCMRN7CQQNTUdhN2OugIteHYwKG6/ldDgoCoytdPqFk2J0QQgghhJgGZk5Q6qI6BwxMOT4nAZfdG+J2qFTk+vA4xrb0d08uhzqpyoAPxuf34fP7jnxAgpIQQgghhJgGZu4Yqc7AlPYV4ow14DfbKPD3LmntVBXKc7zUdSSIJPWxbY4CpWEPk2At2aPSVJVVxx9/5AOKCq7A+DdICCGEEEKILJsCp+VjTHVihsopnncsSqDIPtnv+bBqB5g8/9gNh1OAkrAH11RISYNxBZh6tcyFEEIIIYQ40sztUeqhIs+Ly+0EdzkEiiDaCMl2SMcy2+T7XThVlfqOBFaWXz8/4MbvmgaHQobdCSGEEEKIaWIanJ2PTkHQRcjj7L5Dc0KoFCgFQ7cDU7IDkh2EvODUvBxuS2BY2YlLQY+DXJ/z6BtOIoZp8tprrwFw7LHHoqmdPWESlIQQQgghxDQxo4OSz61RMljxBM0Bvjz7ApCK4U12UOFt4XBDMyndGNXrexwaxcGpUbyhr1g01vsO1QFO78Q0RgghhBBCiCybsUFJUxUqc30ow5lT4/KBy4crWExFvk5NbR2JSBtqqgPFTA3r9R2qQkmOZ/pM6ZHeJCGEEEIIMY3M2KBUkefF5Rh58QTN4WB2eRkHW3NpiaZRk2044w0oemzA5yiApiloikJh0I1zIle17cvp75yTNcIhhe5QVpsjhBBCCCHERJqRQakw6O49L2mEFEWhIteHz5UibbhRlWI0PYoz1oCW7kBVFFQVVMUOR5Or90ixe4E8YfuiOSHeCi17GVFYkrLgQgghhBBiGplxQcnn1igOuY++4TD0Lh3uhtw8SMchUmeHj6zXyRshRe0MRzl2OFL7LKbrzQGlCpr3MKw2OzzgGLvy6UIIIYQQQoy3GRWURjQvaaScXsidA8EUROsh1gSWOfav25eigSdkByN32F4YajCeMOTNhZY9Q2+vzE8SQgghhBDTzIwKSqOdlzQiDheEKyBQAtEGiDWCqY/ta6qO7iF17tDwF4H1hCBvHjTvGjAsuT09euUkKAkhhBBCiGlmxgSlgkB25iWNmOaw12cKFNu9S/GW0RVPOGL/ru5w5AoMPxz15Q5A/nxo2gVW7zLomqpy0okndt5SZH6SEEIIIYSYdmZMUCrK8rykEVNVCBTaF9OEdBRSPS7WMNZm0tz2vCJPGFz+7LfV5bfDUvOugXvBXP4j5zoJIYQQQggxxc2YoDQu85KGS+0srtBz6Foq1hmaIvZXM937OQ5vdzgajwVeXb7OnqWd/YclGXYnhBBCCCGmoRkTlKaMzkVtodC+rSc7A5NuhyPHBPSMOb2QX90ZltIYpsnm1zcDcMxZc5H+JCGEEEIIMd1IUJrsHO6JCUd9OT1Q0BWWEnR0dGApqr1QrRBCCCGEENPMOJeAE1Oaw233LGn2mkm65hl90QghhBBCCCEmIQlKYngcLsivxlCd6KpnolsjhBBCCCHEmJChd2L4NCdRT0nWKpsLIYQQQggx2UhQEiNiKQ6QUXdCCCGEEGKakqF3QgghhBBCCNGH9CiJEXG5XBPdBCGEEEIIIcaMBCUxbJqmcf755090M4QQQgghhBgzMvROCCGEEEIIIfqQoCSEEEIIIYQQfcjQOzFshmHw8ssvA3DSSSehadoEt0gIIYQQQojskqAkRqSpqWmimyCEEEIIIcSYkaF3QgghhBBCCNGHBCUhhBBCCCGE6EOCkhBCCCGEEEL0IUFJCCGEEEIIIfqQoCSEEEIIIYQQfUjVOzEiUhJcCCGEEEJMZxKUxLBpmsbatWsnuhlCCCGEEEKMGRl6J4QQQgghhBB9SFASQgghhBBCiD5k6J0YNtM0eeWVVwA44YQTUFXJ20IIIYQQYnqRoCSGzbIs6uvrM9eFEEIIIYSYbqQrQAghhBBCCCH6kKAkhBBCCCGEEH1IUBJCCCGEEEKIPiQoCSGEEEIIIUQfEpSEEEIIIYQQoo9pX/Wuqypbe3v7BLdk+jAMg1gsBtg/V03TJrhFQgghhBBCHF1XJhhK5eZpH5Q6OjoAqKysnOCWCCGEEEIIISaDjo4OwuHwoNso1jRfCMc0TQ4dOkQwGERRlIluzrTR3t5OZWUlNTU1hEKhiW6OGENyrGcOOdYzhxzrmUOO9cwhx3poLMuio6ODsrIyVHXwWUjTvkdJVVUqKiomuhnTVigUkj/GGUKO9cwhx3rmkGM9c8ixnjnkWB/d0XqSukgxByGEEEIIIYToQ4KSEEIIIYQQQvQhQUmMiNvt5sYbb8Ttdk90U8QYk2M9c8ixnjnkWM8ccqxnDjnW2TftizkIIYQQQgghxHBJj5IQQgghhBBC9CFBSQghhBBCCCH6kKAkhBBCCCGEEH1IUBJCCCGEEEKIPiQoiRG56667qKqqwuPxcPzxx/Pss89OdJPEKD3zzDNcfPHFlJWVoSgKf/7zn3s9blkWN910E2VlZXi9Xt7xjnewdevWiWmsGLHbbruNE044gWAwSFFREZdddhnbtm3rtY0c6+nhxz/+Mcccc0xm8cnVq1fz2GOPZR6X4zx93XbbbSiKwnXXXZe5T4739HDTTTehKEqvS0lJSeZxOc7ZJUFJDNvvf/97rrvuOr72ta/x2muvcfrpp3PhhReyf//+iW6aGIVoNMqKFSv44Q9/2O/j3/nOd7jjjjv44Q9/yCuvvEJJSQnnnnsuHR0d49xSMRrr16/n6quv5qWXXuLJJ59E13XOO+88otFoZhs51tNDRUUF3/72t9mwYQMbNmzgrLPO4tJLL82cNMlxnp5eeeUV7rnnHo455phe98vxnj6WLl3K4cOHM5ctW7ZkHpPjnGWWEMN04oknWp/+9Kd73bdo0SLr+uuvn6AWiWwDrIceeihz2zRNq6SkxPr2t7+duS+RSFjhcNi6++67J6CFIlvq6+stwFq/fr1lWXKsp7vc3FzrZz/7mRznaaqjo8Oqrq62nnzySWvNmjXWtddea1mW/F1PJzfeeKO1YsWKfh+T45x90qMkhiWVSrFx40bOO++8Xvefd955vPDCCxPUKjHW9uzZQ21tba/j7na7WbNmjRz3Ka6trQ2AvLw8QI71dGUYBvfffz/RaJTVq1fLcZ6mrr76at75zndyzjnn9Lpfjvf0smPHDsrKyqiqquL9738/u3fvBuQ4jwXHRDdATC2NjY0YhkFxcXGv+4uLi6mtrZ2gVomx1nVs+zvu+/btm4gmiSywLIsvfOELnHbaaSxbtgyQYz3dbNmyhdWrV5NIJAgEAjz00EMsWbIkc9Ikx3n6uP/++3n11Vd55ZVXjnhM/q6nj5NOOolf/vKXLFiwgLq6Om655RZOOeUUtm7dKsd5DEhQEiOiKEqv25ZlHXGfmH7kuE8v11xzDZs3b+a555474jE51tPDwoUL2bRpE62trTz44INcddVVrF+/PvO4HOfpoaamhmuvvZYnnngCj8cz4HZyvKe+Cy+8MHN9+fLlrF69mnnz5vGLX/yCk08+GZDjnE0y9E4MS0FBAZqmHdF7VF9ff8QnGGL66Nfa/noAAAxdSURBVKqoI8d9+vjc5z7Hww8/zLp166ioqMjcL8d6enG5XMyfP59Vq1Zx2223sWLFCr7//e/LcZ5mNm7cSH19PccffzwOhwOHw8H69eu58847cTgcmWMqx3v68fv9LF++nB07dsjf9RiQoCSGxeVycfzxx/Pkk0/2uv/JJ5/klFNOmaBWibFWVVVFSUlJr+OeSqVYv369HPcpxrIsrrnmGv70pz/x1FNPUVVV1etxOdbTm2VZJJNJOc7TzNlnn82WLVvYtGlT5rJq1Sr+7d/+jU2bNjF37lw53tNUMpnkrbfeorS0VP6ux4AMvRPD9oUvfIEPfehDrFq1itWrV3PPPfewf/9+Pv3pT09008QoRCIRdu7cmbm9Z88eNm3aRF5eHrNmzeK6667j1ltvpbq6murqam699VZ8Ph9XXnnlBLZaDNfVV1/Nb3/7W/7yl78QDAYznzyGw2G8Xm9m7RU51lPfDTfcwIUXXkhlZSUdHR3cf//9PP300zz++ONynKeZYDCYmWfYxe/3k5+fn7lfjvf08MUvfpGLL76YWbNmUV9fzy233EJ7eztXXXWV/F2PhQmrtyemtB/96EfW7NmzLZfLZR133HGZ0sJi6lq3bp0FHHG56qqrLMuyy47eeOONVklJieV2u60zzjjD2rJly8Q2Wgxbf8cYsO69997MNnKsp4ePfexjmffpwsJC6+yzz7aeeOKJzONynKe3nuXBLUuO93RxxRVXWKWlpZbT6bTKysqsd7/73dbWrVszj8txzi7FsixrgjKaEEIIIYQQQkxKMkdJCCGEEEIIIfqQoCSEEEIIIYQQfUhQEkIIIYQQQog+JCgJIYQQQgghRB8SlIQQQgghhBCiDwlKQgghhBBCCNGHBCUhhBBCCCGE6EOCkhBCiCnrpptuYuXKlRPdjKxRFIU///nPAOzduxdFUdi0adOEtkkIIWYqCUpCCCH4yEc+gqIoKIqC0+mkuLiYc889l//3//4fpmlOdPMG9MUvfpF//vOfE92MAT399NMoikJra+uQtj98+DAXXnjh2DZKCCHEkEhQEkIIAcAFF1zA4cOH2bt3L4899hhnnnkm1157LRdddBG6rk908/oVCATIz8+f6GaMWiqVAqCkpAS32z3BrRFCCAESlIQQQnRyu92UlJRQXl7Occcdxw033MBf/vIXHnvsMe67777MdnfccQfLly/H7/dTWVnJZz/7WSKRCADRaJRQKMQDDzzQa9+PPPIIfr+fjo4OUqkU11xzDaWlpXg8HubMmcNtt902YLuefvppTjzxRPx+Pzk5OZx66qns27cPOHLo3Uc+8hEuu+wy/vd//5fS0lLy8/O5+uqrSafTmW2SySRf/vKXqaysxO12U11dzc9//vPM42+++SZr164lEAhQXFzMhz70IRobGwds3759+7j44ovJzc3F7/ezdOlSHn30Ufbu3cuZZ54JQG5uLoqi8JGPfASAd7zjHVxzzTV84QtfoKCggHPPPRfoPfSuL9M0+eQnP8mCBQsy3/8jjzzC8ccfj8fjYe7cudx8882TNtQKIcRUI0FJCCHEgM466yxWrFjBn/70p8x9qqpy55138sYbb/CLX/yCp556ii9/+csA+P1+3v/+93Pvvff22s+9997Le97zHoLBIHfeeScPP/wwf/jDH9i2bRu//vWvmTNnTr+vr+s6l112GWvWrGHz5s28+OKL/Pu//zuKogzY5nXr1rFr1y7WrVvHL37xC+67775eQe/DH/4w999/P3feeSdvvfUWd999N4FAALCHvq1Zs4aVK1eyYcMGHn/8cerq6njf+9434OtdffXVJJNJnnnmGbZs2cLtt99OIBCgsrKSBx98EIBt27Zx+PBhvv/972ee94tf/AKHw8Hzzz/PT37ykwH3D3aP0/ve9z42bNjAc889x+zZs/n73//OBz/4QT7/+c/z5ptv8pOf/IT77ruPb33rW4PuSwghxBBZQgghZryrrrrKuvTSS/t97IorrrAWL1484HP/8Ic/WPn5+ZnbL7/8sqVpmnXw4EHLsiyroaHBcjqd1tNPP21ZlmV97nOfs8466yzLNM2jtqupqckCMs/t68Ybb7RWrFjR6/uYPXu2pet65r73vve91hVXXGFZlmVt27bNAqwnn3yy3/3993//t3Xeeef1uq+mpsYCrG3btvX7nOXLl1s33XRTv4+tW7fOAqyWlpZe969Zs8ZauXLlEdsD1kMPPWRZlmXt2bPHAqxnn33WOuecc6xTTz3Vam1tzWx7+umnW7feemuv5//qV7+ySktL+22LEEKI4ZEeJSGEEIOyLKtXD866des499xzKS8vJxgM8uEPf5impiai0SgAJ554IkuXLuWXv/wlAL/61a+YNWsWZ5xxBmAPj9u0aRMLFy7k85//PE888cSAr52Xl8dHPvIRzj//fC6++GK+//3vc/jw4UHbu3TpUjRNy9wuLS2lvr4egE2bNqFpGmvWrOn3uRs3bmTdunUEAoHMZdGiRQDs2rWr3+d8/vOf55ZbbuHUU0/lxhtvZPPmzYO2r8uqVauGtN0HPvABIpEITzzxBOFwuFdbv/GNb/Rq6yc/+UkOHz5MLBYb0r6FEEIMTIKSEEKIQb311ltUVVUB9nyctWvXsmzZMh588EE2btzIj370I4Be84A+8YlPZIbf3XvvvXz0ox/NhK3jjjuOPXv28M1vfpN4PM773vc+3vOe9wz4+vfeey8vvvgip5xyCr///e9ZsGABL7300oDbO53OXrcVRclU7vN6vYN+r6ZpcvHFF7Np06Zelx07dmSCXl+f+MQn2L17Nx/60IfYsmULq1at4gc/+MGgrwP2MMWhWLt2LZs3bz7iezZNk5tvvrlXO7ds2cKOHTvweDxD2rcQQoiBSVASQggxoKeeeootW7Zw+eWXA7BhwwZ0Xef//u//OPnkk1mwYAGHDh064nkf/OAH2b9/P3feeSdbt27lqquu6vV4KBTiiiuu4Kc//Sm///3vefDBB2lubh6wHcceeyxf/epXeeGFF1i2bBm//e1vR/T9LF++HNM0Wb9+fb+PH3fccWzdupU5c+Ywf/78XpfBgk1lZSWf/vSn+dOf/sR//ud/8tOf/hQAl8sFgGEYI2ovwGc+8xm+/e1vc8kll/Rq93HHHce2bduOaOf8+fNRVfn3LoQQo+WY6AYIIYSYHJLJJLW1tRiGQV1dHY8//ji33XYbF110ER/+8IcBmDdvHrqu84Mf/ICLL76Y559/nrvvvvuIfeXm5vLud7+bL33pS5x33nlUVFRkHvvud79LaWkpK1euRFVV/vjHP1JSUkJOTs4R+9mzZw/33HMPl1xyCWVlZWzbto3t27dn2jNcc+bM4aqrruJjH/sYd955JytWrGDfvn3U19fzvve9j6uvvpqf/vSnfOADH+BLX/oSBQUF7Ny5k/vvv5+f/vSnvYb0dbnuuuu48MILWbBgAS0tLTz11FMsXrwYgNmzZ6MoCn/9619Zu3YtXq83UzhiOD73uc9hGAYXXXQRjz32GKeddhpf//rXueiii6isrOS9730vqqqyefNmtmzZwi233DKin48QQohu8pGTEEIIAB5//HFKS0uZM2cOF1xwAevWrePOO+/kL3/5SyYgrFy5kjvuuIPbb7+dZcuW8Zvf/GbA0t4f//jHSaVSfOxjH+t1fyAQ4Pbbb2fVqlWccMIJ7N27l0cffbTfXhCfz8fbb7/N5ZdfzoIFC/j3f/93rrnmGj71qU+N+Pv88Y9/zHve8x4++9nPsmjRIj75yU9m5leVlZXx/PPPYxgG559/PsuWLePaa68lHA4P2EtjGAZXX301ixcv5oILLmDhwoXcddddAJSXl3PzzTdz/fXXU1xczDXXXDPidl933XXcfPPNrF27lhdeeIHzzz+fv/71rzz55JOccMIJnHzyydxxxx3Mnj17xK8hhBCim2JZljXRjRBCCDH9/OY3v+Haa6/l0KFDmSFoQgghxFQhQ++EEEJkVSwWY8+ePdx222186lOfkpAkhBBiSpKhd0IIIbLqO9/5DitXrqS4uJivfvWrE90cIYQQYkRk6J0QQgghhBBC9CE9SkIIIYQQQgjRhwQlIYQQQgghhOhDgpIQQgghhBBC9CFBSQghhBBCCCH6kKAkhBBCCCGEEH1IUBJCCCGEEEKIPiQoCSGEEEIIIUQfEpSEEEIIIYQQog8JSkIIIYQQQgjRx/8Hri9HYTLcwq0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_two_group(event_res_highdensity, event_res_lowdensity,\n",
    "                       label1='Above Median Population Density',\n",
    "                       label2='Below Median Population Density',\n",
    "                       color1='C0', color2='C1',\n",
    "                       ylabel='Distance (km)',\n",
    "                       outfile='figures/figureX_distance_density.pdf',\n",
    "                       ylim=[-7, 45], xlim=[-7, 56])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure S11B: Distance Results with Time of Strike Subgroups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# subset distance df by morning, day, and evening\n",
    "df_morning = df[df['time_of_day'] == 'morning']\n",
    "df_day = df[df['time_of_day'] == 'day']\n",
    "df_evening = df[df['time_of_day'] == 'evening']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>distance</td>     <th>  R-squared:         </th>  <td>   0.020</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.020</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>     nan</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 21 May 2025</td> <th>  Prob (F-statistic):</th>   <td>   nan</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>12:47:48</td>     <th>  Log-Likelihood:    </th> <td>-2.2008e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>395572</td>      <th>  AIC:               </th>  <td>4.402e+06</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>395491</td>      <th>  BIC:               </th>  <td>4.403e+06</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    80</td>      <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>       <td>cluster</td>     <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>           <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[7]</th>   <td>   41.6112</td> <td>    1.465</td> <td>   28.410</td> <td> 0.000</td> <td>   38.741</td> <td>   44.482</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[8]</th>   <td>    4.4900</td> <td>    1.472</td> <td>    3.051</td> <td> 0.002</td> <td>    1.605</td> <td>    7.375</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[9]</th>   <td>    9.4599</td> <td>    1.484</td> <td>    6.374</td> <td> 0.000</td> <td>    6.551</td> <td>   12.369</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[11]</th>  <td>   12.6022</td> <td>    1.367</td> <td>    9.219</td> <td> 0.000</td> <td>    9.923</td> <td>   15.281</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[12]</th>  <td>   11.9679</td> <td>    1.654</td> <td>    7.237</td> <td> 0.000</td> <td>    8.727</td> <td>   15.209</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[15]</th>  <td>   12.5604</td> <td>    1.084</td> <td>   11.582</td> <td> 0.000</td> <td>   10.435</td> <td>   14.686</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[28]</th>  <td>   21.1463</td> <td>    1.395</td> <td>   15.154</td> <td> 0.000</td> <td>   18.411</td> <td>   23.881</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[38]</th>  <td>    8.2315</td> <td>    1.464</td> <td>    5.621</td> <td> 0.000</td> <td>    5.361</td> <td>   11.102</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[60]</th>  <td>   23.0160</td> <td>    1.374</td> <td>   16.750</td> <td> 0.000</td> <td>   20.323</td> <td>   25.709</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[65]</th>  <td>   60.1578</td> <td>    1.495</td> <td>   40.248</td> <td> 0.000</td> <td>   57.228</td> <td>   63.087</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[67]</th>  <td>   19.4837</td> <td>    1.341</td> <td>   14.526</td> <td> 0.000</td> <td>   16.855</td> <td>   22.113</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[68]</th>  <td>   23.1770</td> <td>    1.334</td> <td>   17.371</td> <td> 0.000</td> <td>   20.562</td> <td>   25.792</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[69]</th>  <td>   20.7978</td> <td>    1.344</td> <td>   15.471</td> <td> 0.000</td> <td>   18.163</td> <td>   23.433</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[70]</th>  <td>   39.2686</td> <td>    1.482</td> <td>   26.490</td> <td> 0.000</td> <td>   36.363</td> <td>   42.174</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[82]</th>  <td>   37.7758</td> <td>    1.456</td> <td>   25.947</td> <td> 0.000</td> <td>   34.922</td> <td>   40.629</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[90]</th>  <td>    5.3127</td> <td>    1.566</td> <td>    3.392</td> <td> 0.001</td> <td>    2.243</td> <td>    8.382</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[95]</th>  <td>   62.0320</td> <td>    1.255</td> <td>   49.441</td> <td> 0.000</td> <td>   59.573</td> <td>   64.491</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[106]</th> <td>    8.1959</td> <td>    0.982</td> <td>    8.348</td> <td> 0.000</td> <td>    6.272</td> <td>   10.120</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_1</th>            <td>   -1.4893</td> <td>    0.835</td> <td>   -1.783</td> <td> 0.075</td> <td>   -3.127</td> <td>    0.148</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_2</th>            <td>   -0.7181</td> <td>    0.794</td> <td>   -0.905</td> <td> 0.366</td> <td>   -2.274</td> <td>    0.838</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_3</th>            <td>   -1.4030</td> <td>    1.001</td> <td>   -1.401</td> <td> 0.161</td> <td>   -3.366</td> <td>    0.560</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_4</th>            <td>   -1.5112</td> <td>    0.913</td> <td>   -1.656</td> <td> 0.098</td> <td>   -3.300</td> <td>    0.278</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_5</th>            <td>    0.2318</td> <td>    0.773</td> <td>    0.300</td> <td> 0.764</td> <td>   -1.283</td> <td>    1.747</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_7</th>            <td>   -0.1809</td> <td>    0.851</td> <td>   -0.213</td> <td> 0.832</td> <td>   -1.849</td> <td>    1.487</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_8</th>            <td>    1.2353</td> <td>    0.851</td> <td>    1.452</td> <td> 0.147</td> <td>   -0.432</td> <td>    2.903</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_9</th>            <td>    2.7174</td> <td>    1.206</td> <td>    2.254</td> <td> 0.024</td> <td>    0.355</td> <td>    5.080</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_10</th>           <td>    2.5927</td> <td>    1.039</td> <td>    2.496</td> <td> 0.013</td> <td>    0.557</td> <td>    4.629</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_11</th>           <td>    2.7739</td> <td>    1.003</td> <td>    2.765</td> <td> 0.006</td> <td>    0.808</td> <td>    4.740</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_12</th>           <td>    3.9298</td> <td>    1.384</td> <td>    2.839</td> <td> 0.005</td> <td>    1.217</td> <td>    6.643</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_13</th>           <td>    4.4326</td> <td>    1.124</td> <td>    3.945</td> <td> 0.000</td> <td>    2.230</td> <td>    6.635</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_14</th>           <td>    4.3500</td> <td>    1.444</td> <td>    3.012</td> <td> 0.003</td> <td>    1.519</td> <td>    7.181</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_15</th>           <td>    5.7430</td> <td>    1.445</td> <td>    3.974</td> <td> 0.000</td> <td>    2.911</td> <td>    8.575</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_16</th>           <td>    5.7440</td> <td>    1.317</td> <td>    4.362</td> <td> 0.000</td> <td>    3.163</td> <td>    8.325</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_17</th>           <td>    5.4930</td> <td>    1.499</td> <td>    3.664</td> <td> 0.000</td> <td>    2.554</td> <td>    8.432</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_18</th>           <td>    5.7804</td> <td>    1.329</td> <td>    4.349</td> <td> 0.000</td> <td>    3.175</td> <td>    8.386</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_19</th>           <td>    6.7271</td> <td>    1.173</td> <td>    5.736</td> <td> 0.000</td> <td>    4.428</td> <td>    9.026</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_20</th>           <td>    7.3070</td> <td>    1.692</td> <td>    4.318</td> <td> 0.000</td> <td>    3.990</td> <td>   10.624</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_21</th>           <td>    7.3574</td> <td>    1.837</td> <td>    4.005</td> <td> 0.000</td> <td>    3.757</td> <td>   10.958</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_22</th>           <td>    7.4815</td> <td>    1.768</td> <td>    4.232</td> <td> 0.000</td> <td>    4.017</td> <td>   10.946</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_23</th>           <td>    7.6254</td> <td>    1.958</td> <td>    3.894</td> <td> 0.000</td> <td>    3.788</td> <td>   11.463</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_24</th>           <td>    7.5229</td> <td>    1.671</td> <td>    4.501</td> <td> 0.000</td> <td>    4.247</td> <td>   10.799</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_25</th>           <td>    9.6766</td> <td>    2.167</td> <td>    4.466</td> <td> 0.000</td> <td>    5.430</td> <td>   13.923</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_26</th>           <td>   10.8388</td> <td>    2.242</td> <td>    4.834</td> <td> 0.000</td> <td>    6.444</td> <td>   15.233</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_27</th>           <td>   11.1575</td> <td>    1.687</td> <td>    6.615</td> <td> 0.000</td> <td>    7.852</td> <td>   14.463</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_28</th>           <td>   10.0979</td> <td>    2.173</td> <td>    4.647</td> <td> 0.000</td> <td>    5.839</td> <td>   14.357</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_29</th>           <td>   11.7615</td> <td>    1.896</td> <td>    6.204</td> <td> 0.000</td> <td>    8.046</td> <td>   15.477</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_30</th>           <td>   11.4218</td> <td>    1.857</td> <td>    6.151</td> <td> 0.000</td> <td>    7.782</td> <td>   15.061</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_31</th>           <td>   11.1057</td> <td>    1.735</td> <td>    6.402</td> <td> 0.000</td> <td>    7.706</td> <td>   14.506</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_32</th>           <td>   12.1405</td> <td>    1.741</td> <td>    6.972</td> <td> 0.000</td> <td>    8.728</td> <td>   15.553</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_33</th>           <td>   11.3373</td> <td>    1.791</td> <td>    6.331</td> <td> 0.000</td> <td>    7.828</td> <td>   14.847</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_34</th>           <td>   12.5951</td> <td>    2.038</td> <td>    6.180</td> <td> 0.000</td> <td>    8.600</td> <td>   16.590</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_35</th>           <td>   12.0485</td> <td>    2.151</td> <td>    5.602</td> <td> 0.000</td> <td>    7.833</td> <td>   16.264</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_36</th>           <td>   12.1923</td> <td>    1.852</td> <td>    6.583</td> <td> 0.000</td> <td>    8.562</td> <td>   15.822</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_37</th>           <td>   12.2220</td> <td>    2.299</td> <td>    5.316</td> <td> 0.000</td> <td>    7.716</td> <td>   16.728</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_38</th>           <td>   12.6039</td> <td>    2.044</td> <td>    6.165</td> <td> 0.000</td> <td>    8.597</td> <td>   16.611</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_39</th>           <td>   12.8189</td> <td>    1.948</td> <td>    6.581</td> <td> 0.000</td> <td>    9.001</td> <td>   16.637</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_40</th>           <td>   13.7630</td> <td>    2.543</td> <td>    5.412</td> <td> 0.000</td> <td>    8.778</td> <td>   18.748</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_41</th>           <td>   13.5509</td> <td>    3.015</td> <td>    4.494</td> <td> 0.000</td> <td>    7.641</td> <td>   19.461</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_42</th>           <td>   13.3168</td> <td>    2.701</td> <td>    4.930</td> <td> 0.000</td> <td>    8.023</td> <td>   18.610</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_43</th>           <td>   13.3819</td> <td>    2.346</td> <td>    5.704</td> <td> 0.000</td> <td>    8.784</td> <td>   17.980</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_44</th>           <td>   13.9858</td> <td>    2.317</td> <td>    6.035</td> <td> 0.000</td> <td>    9.444</td> <td>   18.528</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_45</th>           <td>   14.3015</td> <td>    2.193</td> <td>    6.522</td> <td> 0.000</td> <td>   10.004</td> <td>   18.599</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_46</th>           <td>   14.0048</td> <td>    2.237</td> <td>    6.259</td> <td> 0.000</td> <td>    9.619</td> <td>   18.390</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_47</th>           <td>   13.7882</td> <td>    2.398</td> <td>    5.750</td> <td> 0.000</td> <td>    9.088</td> <td>   18.488</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_48</th>           <td>   12.6275</td> <td>    1.843</td> <td>    6.850</td> <td> 0.000</td> <td>    9.014</td> <td>   16.241</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_49</th>           <td>   13.6786</td> <td>    1.991</td> <td>    6.869</td> <td> 0.000</td> <td>    9.776</td> <td>   17.581</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_50</th>           <td>   12.9369</td> <td>    1.881</td> <td>    6.878</td> <td> 0.000</td> <td>    9.250</td> <td>   16.624</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_51</th>           <td>   14.5452</td> <td>    1.563</td> <td>    9.306</td> <td> 0.000</td> <td>   11.482</td> <td>   17.608</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_52</th>           <td>   12.7946</td> <td>    1.956</td> <td>    6.542</td> <td> 0.000</td> <td>    8.962</td> <td>   16.628</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_53</th>           <td>   13.8096</td> <td>    1.396</td> <td>    9.891</td> <td> 0.000</td> <td>   11.073</td> <td>   16.546</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_54</th>           <td>   14.0252</td> <td>    1.580</td> <td>    8.877</td> <td> 0.000</td> <td>   10.928</td> <td>   17.122</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_55</th>           <td>   13.3390</td> <td>    1.462</td> <td>    9.121</td> <td> 0.000</td> <td>   10.473</td> <td>   16.205</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_56</th>           <td>   13.1677</td> <td>    1.925</td> <td>    6.842</td> <td> 0.000</td> <td>    9.396</td> <td>   16.940</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_57</th>           <td>   13.1874</td> <td>    1.010</td> <td>   13.051</td> <td> 0.000</td> <td>   11.207</td> <td>   15.168</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_58</th>           <td>   13.9196</td> <td>    1.222</td> <td>   11.389</td> <td> 0.000</td> <td>   11.524</td> <td>   16.315</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_59</th>           <td>   13.8997</td> <td>    1.790</td> <td>    7.765</td> <td> 0.000</td> <td>   10.391</td> <td>   17.408</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_60</th>           <td>   14.6860</td> <td>    1.820</td> <td>    8.068</td> <td> 0.000</td> <td>   11.118</td> <td>   18.254</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_61</th>           <td>   14.0503</td> <td>    1.727</td> <td>    8.135</td> <td> 0.000</td> <td>   10.665</td> <td>   17.435</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_62</th>           <td>   15.6340</td> <td>    1.886</td> <td>    8.290</td> <td> 0.000</td> <td>   11.938</td> <td>   19.330</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_63</th>           <td>   16.8597</td> <td>    1.912</td> <td>    8.816</td> <td> 0.000</td> <td>   13.111</td> <td>   20.608</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_64</th>           <td>   11.6149</td> <td>    1.866</td> <td>    6.224</td> <td> 0.000</td> <td>    7.957</td> <td>   15.272</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>428857.527</td> <th>  Durbin-Watson:     </th>   <td>   1.982</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>   <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>30527855.685</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>            <td> 5.656</td>   <th>  Prob(JB):          </th>   <td>    0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>        <td>44.524</td>   <th>  Cond. No.          </th>   <td>    65.9</td>  \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors are robust to cluster correlation (cluster)"
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &     distance     & \\textbf{  R-squared:         } &      0.020    \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &      0.020    \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &        nan    \\\\\n",
       "\\textbf{Date:}             & Wed, 21 May 2025 & \\textbf{  Prob (F-statistic):} &       nan     \\\\\n",
       "\\textbf{Time:}             &     12:47:48     & \\textbf{  Log-Likelihood:    } & -2.2008e+06   \\\\\n",
       "\\textbf{No. Observations:} &      395572      & \\textbf{  AIC:               } &  4.402e+06    \\\\\n",
       "\\textbf{Df Residuals:}     &      395491      & \\textbf{  BIC:               } &  4.403e+06    \\\\\n",
       "\\textbf{Df Model:}         &          80      & \\textbf{                     } &               \\\\\n",
       "\\textbf{Covariance Type:}  &     cluster      & \\textbf{                     } &               \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                        & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{C(strike)[7]}   &      41.6112  &        1.465     &    28.410  &         0.000        &       38.741    &       44.482     \\\\\n",
       "\\textbf{C(strike)[8]}   &       4.4900  &        1.472     &     3.051  &         0.002        &        1.605    &        7.375     \\\\\n",
       "\\textbf{C(strike)[9]}   &       9.4599  &        1.484     &     6.374  &         0.000        &        6.551    &       12.369     \\\\\n",
       "\\textbf{C(strike)[11]}  &      12.6022  &        1.367     &     9.219  &         0.000        &        9.923    &       15.281     \\\\\n",
       "\\textbf{C(strike)[12]}  &      11.9679  &        1.654     &     7.237  &         0.000        &        8.727    &       15.209     \\\\\n",
       "\\textbf{C(strike)[15]}  &      12.5604  &        1.084     &    11.582  &         0.000        &       10.435    &       14.686     \\\\\n",
       "\\textbf{C(strike)[28]}  &      21.1463  &        1.395     &    15.154  &         0.000        &       18.411    &       23.881     \\\\\n",
       "\\textbf{C(strike)[38]}  &       8.2315  &        1.464     &     5.621  &         0.000        &        5.361    &       11.102     \\\\\n",
       "\\textbf{C(strike)[60]}  &      23.0160  &        1.374     &    16.750  &         0.000        &       20.323    &       25.709     \\\\\n",
       "\\textbf{C(strike)[65]}  &      60.1578  &        1.495     &    40.248  &         0.000        &       57.228    &       63.087     \\\\\n",
       "\\textbf{C(strike)[67]}  &      19.4837  &        1.341     &    14.526  &         0.000        &       16.855    &       22.113     \\\\\n",
       "\\textbf{C(strike)[68]}  &      23.1770  &        1.334     &    17.371  &         0.000        &       20.562    &       25.792     \\\\\n",
       "\\textbf{C(strike)[69]}  &      20.7978  &        1.344     &    15.471  &         0.000        &       18.163    &       23.433     \\\\\n",
       "\\textbf{C(strike)[70]}  &      39.2686  &        1.482     &    26.490  &         0.000        &       36.363    &       42.174     \\\\\n",
       "\\textbf{C(strike)[82]}  &      37.7758  &        1.456     &    25.947  &         0.000        &       34.922    &       40.629     \\\\\n",
       "\\textbf{C(strike)[90]}  &       5.3127  &        1.566     &     3.392  &         0.001        &        2.243    &        8.382     \\\\\n",
       "\\textbf{C(strike)[95]}  &      62.0320  &        1.255     &    49.441  &         0.000        &       59.573    &       64.491     \\\\\n",
       "\\textbf{C(strike)[106]} &       8.1959  &        0.982     &     8.348  &         0.000        &        6.272    &       10.120     \\\\\n",
       "\\textbf{X\\_1}           &      -1.4893  &        0.835     &    -1.783  &         0.075        &       -3.127    &        0.148     \\\\\n",
       "\\textbf{X\\_2}           &      -0.7181  &        0.794     &    -0.905  &         0.366        &       -2.274    &        0.838     \\\\\n",
       "\\textbf{X\\_3}           &      -1.4030  &        1.001     &    -1.401  &         0.161        &       -3.366    &        0.560     \\\\\n",
       "\\textbf{X\\_4}           &      -1.5112  &        0.913     &    -1.656  &         0.098        &       -3.300    &        0.278     \\\\\n",
       "\\textbf{X\\_5}           &       0.2318  &        0.773     &     0.300  &         0.764        &       -1.283    &        1.747     \\\\\n",
       "\\textbf{X\\_7}           &      -0.1809  &        0.851     &    -0.213  &         0.832        &       -1.849    &        1.487     \\\\\n",
       "\\textbf{X\\_8}           &       1.2353  &        0.851     &     1.452  &         0.147        &       -0.432    &        2.903     \\\\\n",
       "\\textbf{X\\_9}           &       2.7174  &        1.206     &     2.254  &         0.024        &        0.355    &        5.080     \\\\\n",
       "\\textbf{X\\_10}          &       2.5927  &        1.039     &     2.496  &         0.013        &        0.557    &        4.629     \\\\\n",
       "\\textbf{X\\_11}          &       2.7739  &        1.003     &     2.765  &         0.006        &        0.808    &        4.740     \\\\\n",
       "\\textbf{X\\_12}          &       3.9298  &        1.384     &     2.839  &         0.005        &        1.217    &        6.643     \\\\\n",
       "\\textbf{X\\_13}          &       4.4326  &        1.124     &     3.945  &         0.000        &        2.230    &        6.635     \\\\\n",
       "\\textbf{X\\_14}          &       4.3500  &        1.444     &     3.012  &         0.003        &        1.519    &        7.181     \\\\\n",
       "\\textbf{X\\_15}          &       5.7430  &        1.445     &     3.974  &         0.000        &        2.911    &        8.575     \\\\\n",
       "\\textbf{X\\_16}          &       5.7440  &        1.317     &     4.362  &         0.000        &        3.163    &        8.325     \\\\\n",
       "\\textbf{X\\_17}          &       5.4930  &        1.499     &     3.664  &         0.000        &        2.554    &        8.432     \\\\\n",
       "\\textbf{X\\_18}          &       5.7804  &        1.329     &     4.349  &         0.000        &        3.175    &        8.386     \\\\\n",
       "\\textbf{X\\_19}          &       6.7271  &        1.173     &     5.736  &         0.000        &        4.428    &        9.026     \\\\\n",
       "\\textbf{X\\_20}          &       7.3070  &        1.692     &     4.318  &         0.000        &        3.990    &       10.624     \\\\\n",
       "\\textbf{X\\_21}          &       7.3574  &        1.837     &     4.005  &         0.000        &        3.757    &       10.958     \\\\\n",
       "\\textbf{X\\_22}          &       7.4815  &        1.768     &     4.232  &         0.000        &        4.017    &       10.946     \\\\\n",
       "\\textbf{X\\_23}          &       7.6254  &        1.958     &     3.894  &         0.000        &        3.788    &       11.463     \\\\\n",
       "\\textbf{X\\_24}          &       7.5229  &        1.671     &     4.501  &         0.000        &        4.247    &       10.799     \\\\\n",
       "\\textbf{X\\_25}          &       9.6766  &        2.167     &     4.466  &         0.000        &        5.430    &       13.923     \\\\\n",
       "\\textbf{X\\_26}          &      10.8388  &        2.242     &     4.834  &         0.000        &        6.444    &       15.233     \\\\\n",
       "\\textbf{X\\_27}          &      11.1575  &        1.687     &     6.615  &         0.000        &        7.852    &       14.463     \\\\\n",
       "\\textbf{X\\_28}          &      10.0979  &        2.173     &     4.647  &         0.000        &        5.839    &       14.357     \\\\\n",
       "\\textbf{X\\_29}          &      11.7615  &        1.896     &     6.204  &         0.000        &        8.046    &       15.477     \\\\\n",
       "\\textbf{X\\_30}          &      11.4218  &        1.857     &     6.151  &         0.000        &        7.782    &       15.061     \\\\\n",
       "\\textbf{X\\_31}          &      11.1057  &        1.735     &     6.402  &         0.000        &        7.706    &       14.506     \\\\\n",
       "\\textbf{X\\_32}          &      12.1405  &        1.741     &     6.972  &         0.000        &        8.728    &       15.553     \\\\\n",
       "\\textbf{X\\_33}          &      11.3373  &        1.791     &     6.331  &         0.000        &        7.828    &       14.847     \\\\\n",
       "\\textbf{X\\_34}          &      12.5951  &        2.038     &     6.180  &         0.000        &        8.600    &       16.590     \\\\\n",
       "\\textbf{X\\_35}          &      12.0485  &        2.151     &     5.602  &         0.000        &        7.833    &       16.264     \\\\\n",
       "\\textbf{X\\_36}          &      12.1923  &        1.852     &     6.583  &         0.000        &        8.562    &       15.822     \\\\\n",
       "\\textbf{X\\_37}          &      12.2220  &        2.299     &     5.316  &         0.000        &        7.716    &       16.728     \\\\\n",
       "\\textbf{X\\_38}          &      12.6039  &        2.044     &     6.165  &         0.000        &        8.597    &       16.611     \\\\\n",
       "\\textbf{X\\_39}          &      12.8189  &        1.948     &     6.581  &         0.000        &        9.001    &       16.637     \\\\\n",
       "\\textbf{X\\_40}          &      13.7630  &        2.543     &     5.412  &         0.000        &        8.778    &       18.748     \\\\\n",
       "\\textbf{X\\_41}          &      13.5509  &        3.015     &     4.494  &         0.000        &        7.641    &       19.461     \\\\\n",
       "\\textbf{X\\_42}          &      13.3168  &        2.701     &     4.930  &         0.000        &        8.023    &       18.610     \\\\\n",
       "\\textbf{X\\_43}          &      13.3819  &        2.346     &     5.704  &         0.000        &        8.784    &       17.980     \\\\\n",
       "\\textbf{X\\_44}          &      13.9858  &        2.317     &     6.035  &         0.000        &        9.444    &       18.528     \\\\\n",
       "\\textbf{X\\_45}          &      14.3015  &        2.193     &     6.522  &         0.000        &       10.004    &       18.599     \\\\\n",
       "\\textbf{X\\_46}          &      14.0048  &        2.237     &     6.259  &         0.000        &        9.619    &       18.390     \\\\\n",
       "\\textbf{X\\_47}          &      13.7882  &        2.398     &     5.750  &         0.000        &        9.088    &       18.488     \\\\\n",
       "\\textbf{X\\_48}          &      12.6275  &        1.843     &     6.850  &         0.000        &        9.014    &       16.241     \\\\\n",
       "\\textbf{X\\_49}          &      13.6786  &        1.991     &     6.869  &         0.000        &        9.776    &       17.581     \\\\\n",
       "\\textbf{X\\_50}          &      12.9369  &        1.881     &     6.878  &         0.000        &        9.250    &       16.624     \\\\\n",
       "\\textbf{X\\_51}          &      14.5452  &        1.563     &     9.306  &         0.000        &       11.482    &       17.608     \\\\\n",
       "\\textbf{X\\_52}          &      12.7946  &        1.956     &     6.542  &         0.000        &        8.962    &       16.628     \\\\\n",
       "\\textbf{X\\_53}          &      13.8096  &        1.396     &     9.891  &         0.000        &       11.073    &       16.546     \\\\\n",
       "\\textbf{X\\_54}          &      14.0252  &        1.580     &     8.877  &         0.000        &       10.928    &       17.122     \\\\\n",
       "\\textbf{X\\_55}          &      13.3390  &        1.462     &     9.121  &         0.000        &       10.473    &       16.205     \\\\\n",
       "\\textbf{X\\_56}          &      13.1677  &        1.925     &     6.842  &         0.000        &        9.396    &       16.940     \\\\\n",
       "\\textbf{X\\_57}          &      13.1874  &        1.010     &    13.051  &         0.000        &       11.207    &       15.168     \\\\\n",
       "\\textbf{X\\_58}          &      13.9196  &        1.222     &    11.389  &         0.000        &       11.524    &       16.315     \\\\\n",
       "\\textbf{X\\_59}          &      13.8997  &        1.790     &     7.765  &         0.000        &       10.391    &       17.408     \\\\\n",
       "\\textbf{X\\_60}          &      14.6860  &        1.820     &     8.068  &         0.000        &       11.118    &       18.254     \\\\\n",
       "\\textbf{X\\_61}          &      14.0503  &        1.727     &     8.135  &         0.000        &       10.665    &       17.435     \\\\\n",
       "\\textbf{X\\_62}          &      15.6340  &        1.886     &     8.290  &         0.000        &       11.938    &       19.330     \\\\\n",
       "\\textbf{X\\_63}          &      16.8597  &        1.912     &     8.816  &         0.000        &       13.111    &       20.608     \\\\\n",
       "\\textbf{X\\_64}          &      11.6149  &        1.866     &     6.224  &         0.000        &        7.957    &       15.272     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 428857.527 & \\textbf{  Durbin-Watson:     } &      1.982    \\\\\n",
       "\\textbf{Prob(Omnibus):} &    0.000   & \\textbf{  Jarque-Bera (JB):  } & 30527855.685  \\\\\n",
       "\\textbf{Skew:}          &    5.656   & \\textbf{  Prob(JB):          } &       0.00    \\\\\n",
       "\\textbf{Kurtosis:}      &   44.524   & \\textbf{  Cond. No.          } &       65.9    \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors are robust to cluster correlation (cluster)"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:               distance   R-squared:                       0.020\n",
       "Model:                            OLS   Adj. R-squared:                  0.020\n",
       "Method:                 Least Squares   F-statistic:                       nan\n",
       "Date:                Wed, 21 May 2025   Prob (F-statistic):                nan\n",
       "Time:                        12:47:48   Log-Likelihood:            -2.2008e+06\n",
       "No. Observations:              395572   AIC:                         4.402e+06\n",
       "Df Residuals:                  395491   BIC:                         4.403e+06\n",
       "Df Model:                          80                                         \n",
       "Covariance Type:              cluster                                         \n",
       "==================================================================================\n",
       "                     coef    std err          z      P>|z|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------\n",
       "C(strike)[7]      41.6112      1.465     28.410      0.000      38.741      44.482\n",
       "C(strike)[8]       4.4900      1.472      3.051      0.002       1.605       7.375\n",
       "C(strike)[9]       9.4599      1.484      6.374      0.000       6.551      12.369\n",
       "C(strike)[11]     12.6022      1.367      9.219      0.000       9.923      15.281\n",
       "C(strike)[12]     11.9679      1.654      7.237      0.000       8.727      15.209\n",
       "C(strike)[15]     12.5604      1.084     11.582      0.000      10.435      14.686\n",
       "C(strike)[28]     21.1463      1.395     15.154      0.000      18.411      23.881\n",
       "C(strike)[38]      8.2315      1.464      5.621      0.000       5.361      11.102\n",
       "C(strike)[60]     23.0160      1.374     16.750      0.000      20.323      25.709\n",
       "C(strike)[65]     60.1578      1.495     40.248      0.000      57.228      63.087\n",
       "C(strike)[67]     19.4837      1.341     14.526      0.000      16.855      22.113\n",
       "C(strike)[68]     23.1770      1.334     17.371      0.000      20.562      25.792\n",
       "C(strike)[69]     20.7978      1.344     15.471      0.000      18.163      23.433\n",
       "C(strike)[70]     39.2686      1.482     26.490      0.000      36.363      42.174\n",
       "C(strike)[82]     37.7758      1.456     25.947      0.000      34.922      40.629\n",
       "C(strike)[90]      5.3127      1.566      3.392      0.001       2.243       8.382\n",
       "C(strike)[95]     62.0320      1.255     49.441      0.000      59.573      64.491\n",
       "C(strike)[106]     8.1959      0.982      8.348      0.000       6.272      10.120\n",
       "X_1               -1.4893      0.835     -1.783      0.075      -3.127       0.148\n",
       "X_2               -0.7181      0.794     -0.905      0.366      -2.274       0.838\n",
       "X_3               -1.4030      1.001     -1.401      0.161      -3.366       0.560\n",
       "X_4               -1.5112      0.913     -1.656      0.098      -3.300       0.278\n",
       "X_5                0.2318      0.773      0.300      0.764      -1.283       1.747\n",
       "X_7               -0.1809      0.851     -0.213      0.832      -1.849       1.487\n",
       "X_8                1.2353      0.851      1.452      0.147      -0.432       2.903\n",
       "X_9                2.7174      1.206      2.254      0.024       0.355       5.080\n",
       "X_10               2.5927      1.039      2.496      0.013       0.557       4.629\n",
       "X_11               2.7739      1.003      2.765      0.006       0.808       4.740\n",
       "X_12               3.9298      1.384      2.839      0.005       1.217       6.643\n",
       "X_13               4.4326      1.124      3.945      0.000       2.230       6.635\n",
       "X_14               4.3500      1.444      3.012      0.003       1.519       7.181\n",
       "X_15               5.7430      1.445      3.974      0.000       2.911       8.575\n",
       "X_16               5.7440      1.317      4.362      0.000       3.163       8.325\n",
       "X_17               5.4930      1.499      3.664      0.000       2.554       8.432\n",
       "X_18               5.7804      1.329      4.349      0.000       3.175       8.386\n",
       "X_19               6.7271      1.173      5.736      0.000       4.428       9.026\n",
       "X_20               7.3070      1.692      4.318      0.000       3.990      10.624\n",
       "X_21               7.3574      1.837      4.005      0.000       3.757      10.958\n",
       "X_22               7.4815      1.768      4.232      0.000       4.017      10.946\n",
       "X_23               7.6254      1.958      3.894      0.000       3.788      11.463\n",
       "X_24               7.5229      1.671      4.501      0.000       4.247      10.799\n",
       "X_25               9.6766      2.167      4.466      0.000       5.430      13.923\n",
       "X_26              10.8388      2.242      4.834      0.000       6.444      15.233\n",
       "X_27              11.1575      1.687      6.615      0.000       7.852      14.463\n",
       "X_28              10.0979      2.173      4.647      0.000       5.839      14.357\n",
       "X_29              11.7615      1.896      6.204      0.000       8.046      15.477\n",
       "X_30              11.4218      1.857      6.151      0.000       7.782      15.061\n",
       "X_31              11.1057      1.735      6.402      0.000       7.706      14.506\n",
       "X_32              12.1405      1.741      6.972      0.000       8.728      15.553\n",
       "X_33              11.3373      1.791      6.331      0.000       7.828      14.847\n",
       "X_34              12.5951      2.038      6.180      0.000       8.600      16.590\n",
       "X_35              12.0485      2.151      5.602      0.000       7.833      16.264\n",
       "X_36              12.1923      1.852      6.583      0.000       8.562      15.822\n",
       "X_37              12.2220      2.299      5.316      0.000       7.716      16.728\n",
       "X_38              12.6039      2.044      6.165      0.000       8.597      16.611\n",
       "X_39              12.8189      1.948      6.581      0.000       9.001      16.637\n",
       "X_40              13.7630      2.543      5.412      0.000       8.778      18.748\n",
       "X_41              13.5509      3.015      4.494      0.000       7.641      19.461\n",
       "X_42              13.3168      2.701      4.930      0.000       8.023      18.610\n",
       "X_43              13.3819      2.346      5.704      0.000       8.784      17.980\n",
       "X_44              13.9858      2.317      6.035      0.000       9.444      18.528\n",
       "X_45              14.3015      2.193      6.522      0.000      10.004      18.599\n",
       "X_46              14.0048      2.237      6.259      0.000       9.619      18.390\n",
       "X_47              13.7882      2.398      5.750      0.000       9.088      18.488\n",
       "X_48              12.6275      1.843      6.850      0.000       9.014      16.241\n",
       "X_49              13.6786      1.991      6.869      0.000       9.776      17.581\n",
       "X_50              12.9369      1.881      6.878      0.000       9.250      16.624\n",
       "X_51              14.5452      1.563      9.306      0.000      11.482      17.608\n",
       "X_52              12.7946      1.956      6.542      0.000       8.962      16.628\n",
       "X_53              13.8096      1.396      9.891      0.000      11.073      16.546\n",
       "X_54              14.0252      1.580      8.877      0.000      10.928      17.122\n",
       "X_55              13.3390      1.462      9.121      0.000      10.473      16.205\n",
       "X_56              13.1677      1.925      6.842      0.000       9.396      16.940\n",
       "X_57              13.1874      1.010     13.051      0.000      11.207      15.168\n",
       "X_58              13.9196      1.222     11.389      0.000      11.524      16.315\n",
       "X_59              13.8997      1.790      7.765      0.000      10.391      17.408\n",
       "X_60              14.6860      1.820      8.068      0.000      11.118      18.254\n",
       "X_61              14.0503      1.727      8.135      0.000      10.665      17.435\n",
       "X_62              15.6340      1.886      8.290      0.000      11.938      19.330\n",
       "X_63              16.8597      1.912      8.816      0.000      13.111      20.608\n",
       "X_64              11.6149      1.866      6.224      0.000       7.957      15.272\n",
       "==============================================================================\n",
       "Omnibus:                   428857.527   Durbin-Watson:                   1.982\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):         30527855.685\n",
       "Skew:                           5.656   Prob(JB):                         0.00\n",
       "Kurtosis:                      44.524   Cond. No.                         65.9\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors are robust to cluster correlation (cluster)\n",
       "\"\"\""
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# run the event study regression for distance around strikes\n",
    "# 7 lags and 56 leads\n",
    "# time vars provide the parameters of interest\n",
    "# drop the day = -2 variable for difference in means and to avoid multicollinearity\n",
    "# use strike fixed effects and cluster SE by strike\n",
    "reg = sm.ols('distance ~ ' + var_form + ' + C(strike) - 1', \n",
    "             data=df_morning).fit(cov_type='cluster',\n",
    "                          cov_kwds={'groups': np.array(df_morning[['strike']])})\n",
    "\n",
    "\n",
    "\n",
    "# save parameters and confidence intervals for plotting\n",
    "conf_int = reg.conf_int()\n",
    "res_morning = pd.DataFrame({\n",
    "    'params': reg.params,\n",
    "    'ci_l': conf_int[0],  # lower bound\n",
    "    'ci_h': conf_int[1],  # upper bound\n",
    "})\n",
    "\n",
    "# print regression summary\n",
    "reg.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>distance</td>     <th>  R-squared:         </th>  <td>   0.030</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.030</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>     nan</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 21 May 2025</td> <th>  Prob (F-statistic):</th>   <td>   nan</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>12:48:13</td>     <th>  Log-Likelihood:    </th> <td>-5.1496e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>902086</td>      <th>  AIC:               </th>  <td>1.030e+07</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>901995</td>      <th>  BIC:               </th>  <td>1.030e+07</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    90</td>      <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>       <td>cluster</td>     <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>           <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[1]</th>   <td>   15.2388</td> <td>    2.002</td> <td>    7.610</td> <td> 0.000</td> <td>   11.314</td> <td>   19.163</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[3]</th>   <td>   12.8538</td> <td>    2.046</td> <td>    6.282</td> <td> 0.000</td> <td>    8.844</td> <td>   16.864</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[4]</th>   <td>    8.2298</td> <td>    2.003</td> <td>    4.109</td> <td> 0.000</td> <td>    4.304</td> <td>   12.156</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[6]</th>   <td>   24.4013</td> <td>    2.086</td> <td>   11.700</td> <td> 0.000</td> <td>   20.314</td> <td>   28.489</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[10]</th>  <td>   16.1397</td> <td>    1.860</td> <td>    8.679</td> <td> 0.000</td> <td>   12.495</td> <td>   19.784</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[14]</th>  <td>   20.0794</td> <td>    1.990</td> <td>   10.092</td> <td> 0.000</td> <td>   16.180</td> <td>   23.979</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[20]</th>  <td>   26.1933</td> <td>    1.803</td> <td>   14.526</td> <td> 0.000</td> <td>   22.659</td> <td>   29.727</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[31]</th>  <td>   10.9714</td> <td>    2.161</td> <td>    5.078</td> <td> 0.000</td> <td>    6.736</td> <td>   15.206</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[34]</th>  <td>   11.5815</td> <td>    2.050</td> <td>    5.648</td> <td> 0.000</td> <td>    7.563</td> <td>   15.600</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[37]</th>  <td>    6.4713</td> <td>    2.059</td> <td>    3.142</td> <td> 0.002</td> <td>    2.435</td> <td>   10.508</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[39]</th>  <td>    8.3024</td> <td>    2.041</td> <td>    4.067</td> <td> 0.000</td> <td>    4.301</td> <td>   12.304</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[40]</th>  <td>   28.2648</td> <td>    2.093</td> <td>   13.505</td> <td> 0.000</td> <td>   24.163</td> <td>   32.367</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[42]</th>  <td>   18.9955</td> <td>    2.063</td> <td>    9.209</td> <td> 0.000</td> <td>   14.952</td> <td>   23.039</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[43]</th>  <td>   20.8110</td> <td>    1.869</td> <td>   11.132</td> <td> 0.000</td> <td>   17.147</td> <td>   24.475</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[47]</th>  <td>   13.6480</td> <td>    2.043</td> <td>    6.680</td> <td> 0.000</td> <td>    9.643</td> <td>   17.653</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[49]</th>  <td>   16.5403</td> <td>    2.040</td> <td>    8.109</td> <td> 0.000</td> <td>   12.542</td> <td>   20.538</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[57]</th>  <td>   15.4428</td> <td>    2.068</td> <td>    7.466</td> <td> 0.000</td> <td>   11.389</td> <td>   19.497</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[58]</th>  <td>   24.3538</td> <td>    1.870</td> <td>   13.022</td> <td> 0.000</td> <td>   20.688</td> <td>   28.019</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[59]</th>  <td>   16.7929</td> <td>    2.107</td> <td>    7.970</td> <td> 0.000</td> <td>   12.663</td> <td>   20.923</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[62]</th>  <td>   43.5397</td> <td>    2.014</td> <td>   21.622</td> <td> 0.000</td> <td>   39.593</td> <td>   47.487</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[71]</th>  <td>   69.1997</td> <td>    1.943</td> <td>   35.615</td> <td> 0.000</td> <td>   65.391</td> <td>   73.008</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[72]</th>  <td>   14.6578</td> <td>    2.079</td> <td>    7.052</td> <td> 0.000</td> <td>   10.584</td> <td>   18.732</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[75]</th>  <td>    5.0884</td> <td>    2.046</td> <td>    2.487</td> <td> 0.013</td> <td>    1.079</td> <td>    9.098</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[76]</th>  <td>   32.7346</td> <td>    1.975</td> <td>   16.575</td> <td> 0.000</td> <td>   28.864</td> <td>   36.605</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[83]</th>  <td>   26.7067</td> <td>    1.801</td> <td>   14.829</td> <td> 0.000</td> <td>   23.177</td> <td>   30.236</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[85]</th>  <td>   35.8376</td> <td>    1.851</td> <td>   19.358</td> <td> 0.000</td> <td>   32.209</td> <td>   39.466</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[87]</th>  <td>    9.4218</td> <td>    1.598</td> <td>    5.897</td> <td> 0.000</td> <td>    6.290</td> <td>   12.553</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[107]</th> <td>   51.0777</td> <td>    1.140</td> <td>   44.806</td> <td> 0.000</td> <td>   48.843</td> <td>   53.312</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_1</th>            <td>   -2.1785</td> <td>    2.583</td> <td>   -0.843</td> <td> 0.399</td> <td>   -7.241</td> <td>    2.884</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_2</th>            <td>   -0.3462</td> <td>    1.894</td> <td>   -0.183</td> <td> 0.855</td> <td>   -4.058</td> <td>    3.365</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_3</th>            <td>   -2.7391</td> <td>    2.319</td> <td>   -1.181</td> <td> 0.238</td> <td>   -7.284</td> <td>    1.806</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_4</th>            <td>   -2.3871</td> <td>    2.076</td> <td>   -1.150</td> <td> 0.250</td> <td>   -6.456</td> <td>    1.682</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_5</th>            <td>   -1.2667</td> <td>    0.732</td> <td>   -1.731</td> <td> 0.083</td> <td>   -2.701</td> <td>    0.167</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_7</th>            <td>    0.7400</td> <td>    0.905</td> <td>    0.817</td> <td> 0.414</td> <td>   -1.035</td> <td>    2.515</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_8</th>            <td>    0.0288</td> <td>    0.667</td> <td>    0.043</td> <td> 0.966</td> <td>   -1.279</td> <td>    1.337</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_9</th>            <td>    2.4144</td> <td>    1.297</td> <td>    1.862</td> <td> 0.063</td> <td>   -0.127</td> <td>    4.956</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_10</th>           <td>    3.1905</td> <td>    1.481</td> <td>    2.154</td> <td> 0.031</td> <td>    0.287</td> <td>    6.094</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_11</th>           <td>    4.4734</td> <td>    1.437</td> <td>    3.112</td> <td> 0.002</td> <td>    1.656</td> <td>    7.291</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_12</th>           <td>    4.8928</td> <td>    1.706</td> <td>    2.868</td> <td> 0.004</td> <td>    1.549</td> <td>    8.237</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_13</th>           <td>    4.3686</td> <td>    1.606</td> <td>    2.719</td> <td> 0.007</td> <td>    1.220</td> <td>    7.517</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_14</th>           <td>    6.9791</td> <td>    1.986</td> <td>    3.514</td> <td> 0.000</td> <td>    3.086</td> <td>   10.872</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_15</th>           <td>    5.3628</td> <td>    1.822</td> <td>    2.944</td> <td> 0.003</td> <td>    1.792</td> <td>    8.933</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_16</th>           <td>    6.8669</td> <td>    1.805</td> <td>    3.804</td> <td> 0.000</td> <td>    3.329</td> <td>   10.405</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_17</th>           <td>    8.4558</td> <td>    2.007</td> <td>    4.213</td> <td> 0.000</td> <td>    4.522</td> <td>   12.389</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_18</th>           <td>    8.0113</td> <td>    2.345</td> <td>    3.416</td> <td> 0.001</td> <td>    3.415</td> <td>   12.607</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_19</th>           <td>    6.6825</td> <td>    1.528</td> <td>    4.373</td> <td> 0.000</td> <td>    3.687</td> <td>    9.678</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_20</th>           <td>    7.4897</td> <td>    1.678</td> <td>    4.465</td> <td> 0.000</td> <td>    4.202</td> <td>   10.778</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_21</th>           <td>    8.3515</td> <td>    1.546</td> <td>    5.402</td> <td> 0.000</td> <td>    5.322</td> <td>   11.381</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_22</th>           <td>    9.0712</td> <td>    1.955</td> <td>    4.639</td> <td> 0.000</td> <td>    5.239</td> <td>   12.904</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_23</th>           <td>    9.1432</td> <td>    1.741</td> <td>    5.251</td> <td> 0.000</td> <td>    5.730</td> <td>   12.556</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_24</th>           <td>    9.5974</td> <td>    2.014</td> <td>    4.765</td> <td> 0.000</td> <td>    5.650</td> <td>   13.545</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_25</th>           <td>    9.4651</td> <td>    2.217</td> <td>    4.269</td> <td> 0.000</td> <td>    5.120</td> <td>   13.811</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_26</th>           <td>    9.5434</td> <td>    2.286</td> <td>    4.175</td> <td> 0.000</td> <td>    5.064</td> <td>   14.023</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_27</th>           <td>    9.2764</td> <td>    1.964</td> <td>    4.723</td> <td> 0.000</td> <td>    5.427</td> <td>   13.126</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_28</th>           <td>    9.8951</td> <td>    1.792</td> <td>    5.522</td> <td> 0.000</td> <td>    6.383</td> <td>   13.407</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_29</th>           <td>   12.0668</td> <td>    2.404</td> <td>    5.019</td> <td> 0.000</td> <td>    7.355</td> <td>   16.779</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_30</th>           <td>   11.5320</td> <td>    2.242</td> <td>    5.143</td> <td> 0.000</td> <td>    7.137</td> <td>   15.927</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_31</th>           <td>   11.1310</td> <td>    2.429</td> <td>    4.583</td> <td> 0.000</td> <td>    6.371</td> <td>   15.891</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_32</th>           <td>   10.7441</td> <td>    2.527</td> <td>    4.252</td> <td> 0.000</td> <td>    5.792</td> <td>   15.696</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_33</th>           <td>   10.6800</td> <td>    2.622</td> <td>    4.073</td> <td> 0.000</td> <td>    5.540</td> <td>   15.820</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_34</th>           <td>   11.2172</td> <td>    2.120</td> <td>    5.290</td> <td> 0.000</td> <td>    7.062</td> <td>   15.373</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_35</th>           <td>   12.0441</td> <td>    2.384</td> <td>    5.052</td> <td> 0.000</td> <td>    7.372</td> <td>   16.716</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_36</th>           <td>   11.7754</td> <td>    2.708</td> <td>    4.348</td> <td> 0.000</td> <td>    6.467</td> <td>   17.084</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_37</th>           <td>   12.8206</td> <td>    2.825</td> <td>    4.538</td> <td> 0.000</td> <td>    7.284</td> <td>   18.357</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_38</th>           <td>   13.1606</td> <td>    2.903</td> <td>    4.533</td> <td> 0.000</td> <td>    7.470</td> <td>   18.851</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_39</th>           <td>   13.1852</td> <td>    3.223</td> <td>    4.090</td> <td> 0.000</td> <td>    6.868</td> <td>   19.503</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_40</th>           <td>   12.7498</td> <td>    4.148</td> <td>    3.074</td> <td> 0.002</td> <td>    4.620</td> <td>   20.879</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_41</th>           <td>   12.1759</td> <td>    2.946</td> <td>    4.132</td> <td> 0.000</td> <td>    6.401</td> <td>   17.951</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_42</th>           <td>   13.0152</td> <td>    3.437</td> <td>    3.787</td> <td> 0.000</td> <td>    6.279</td> <td>   19.752</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_43</th>           <td>   12.8419</td> <td>    3.089</td> <td>    4.157</td> <td> 0.000</td> <td>    6.787</td> <td>   18.897</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_44</th>           <td>   13.9163</td> <td>    3.003</td> <td>    4.634</td> <td> 0.000</td> <td>    8.031</td> <td>   19.802</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_45</th>           <td>   13.7291</td> <td>    3.411</td> <td>    4.025</td> <td> 0.000</td> <td>    7.044</td> <td>   20.415</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_46</th>           <td>   13.5023</td> <td>    3.346</td> <td>    4.036</td> <td> 0.000</td> <td>    6.945</td> <td>   20.060</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_47</th>           <td>   14.2479</td> <td>    3.977</td> <td>    3.583</td> <td> 0.000</td> <td>    6.454</td> <td>   22.042</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_48</th>           <td>   15.0119</td> <td>    3.787</td> <td>    3.964</td> <td> 0.000</td> <td>    7.589</td> <td>   22.435</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_49</th>           <td>   15.2769</td> <td>    3.608</td> <td>    4.235</td> <td> 0.000</td> <td>    8.206</td> <td>   22.348</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_50</th>           <td>   15.1319</td> <td>    3.380</td> <td>    4.476</td> <td> 0.000</td> <td>    8.506</td> <td>   21.757</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_51</th>           <td>   14.6213</td> <td>    3.334</td> <td>    4.386</td> <td> 0.000</td> <td>    8.087</td> <td>   21.156</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_52</th>           <td>   15.8159</td> <td>    3.738</td> <td>    4.231</td> <td> 0.000</td> <td>    8.489</td> <td>   23.143</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_53</th>           <td>   15.4150</td> <td>    3.584</td> <td>    4.301</td> <td> 0.000</td> <td>    8.391</td> <td>   22.439</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_54</th>           <td>   14.8774</td> <td>    4.539</td> <td>    3.277</td> <td> 0.001</td> <td>    5.980</td> <td>   23.775</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_55</th>           <td>   15.5185</td> <td>    3.198</td> <td>    4.852</td> <td> 0.000</td> <td>    9.250</td> <td>   21.787</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_56</th>           <td>   15.8883</td> <td>    3.593</td> <td>    4.422</td> <td> 0.000</td> <td>    8.847</td> <td>   22.930</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_57</th>           <td>   16.1304</td> <td>    3.465</td> <td>    4.656</td> <td> 0.000</td> <td>    9.340</td> <td>   22.921</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_58</th>           <td>   16.0652</td> <td>    3.555</td> <td>    4.519</td> <td> 0.000</td> <td>    9.098</td> <td>   23.033</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_59</th>           <td>   15.2721</td> <td>    3.163</td> <td>    4.828</td> <td> 0.000</td> <td>    9.072</td> <td>   21.472</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_60</th>           <td>   16.6333</td> <td>    4.443</td> <td>    3.744</td> <td> 0.000</td> <td>    7.925</td> <td>   25.342</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_61</th>           <td>   15.0572</td> <td>    3.521</td> <td>    4.276</td> <td> 0.000</td> <td>    8.156</td> <td>   21.959</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_62</th>           <td>   15.9543</td> <td>    3.328</td> <td>    4.794</td> <td> 0.000</td> <td>    9.431</td> <td>   22.477</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_63</th>           <td>   14.6722</td> <td>    3.134</td> <td>    4.681</td> <td> 0.000</td> <td>    8.529</td> <td>   20.816</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_64</th>           <td>   16.1679</td> <td>    3.593</td> <td>    4.500</td> <td> 0.000</td> <td>    9.125</td> <td>   23.210</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>908982.476</td> <th>  Durbin-Watson:     </th>   <td>   1.980</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>   <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>47855945.075</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>            <td> 5.105</td>   <th>  Prob(JB):          </th>   <td>    0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>        <td>37.190</td>   <th>  Cond. No.          </th>   <td>    40.6</td>  \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors are robust to cluster correlation (cluster)"
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &     distance     & \\textbf{  R-squared:         } &      0.030    \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &      0.030    \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &        nan    \\\\\n",
       "\\textbf{Date:}             & Wed, 21 May 2025 & \\textbf{  Prob (F-statistic):} &       nan     \\\\\n",
       "\\textbf{Time:}             &     12:48:13     & \\textbf{  Log-Likelihood:    } & -5.1496e+06   \\\\\n",
       "\\textbf{No. Observations:} &      902086      & \\textbf{  AIC:               } &  1.030e+07    \\\\\n",
       "\\textbf{Df Residuals:}     &      901995      & \\textbf{  BIC:               } &  1.030e+07    \\\\\n",
       "\\textbf{Df Model:}         &          90      & \\textbf{                     } &               \\\\\n",
       "\\textbf{Covariance Type:}  &     cluster      & \\textbf{                     } &               \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                        & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{C(strike)[1]}   &      15.2388  &        2.002     &     7.610  &         0.000        &       11.314    &       19.163     \\\\\n",
       "\\textbf{C(strike)[3]}   &      12.8538  &        2.046     &     6.282  &         0.000        &        8.844    &       16.864     \\\\\n",
       "\\textbf{C(strike)[4]}   &       8.2298  &        2.003     &     4.109  &         0.000        &        4.304    &       12.156     \\\\\n",
       "\\textbf{C(strike)[6]}   &      24.4013  &        2.086     &    11.700  &         0.000        &       20.314    &       28.489     \\\\\n",
       "\\textbf{C(strike)[10]}  &      16.1397  &        1.860     &     8.679  &         0.000        &       12.495    &       19.784     \\\\\n",
       "\\textbf{C(strike)[14]}  &      20.0794  &        1.990     &    10.092  &         0.000        &       16.180    &       23.979     \\\\\n",
       "\\textbf{C(strike)[20]}  &      26.1933  &        1.803     &    14.526  &         0.000        &       22.659    &       29.727     \\\\\n",
       "\\textbf{C(strike)[31]}  &      10.9714  &        2.161     &     5.078  &         0.000        &        6.736    &       15.206     \\\\\n",
       "\\textbf{C(strike)[34]}  &      11.5815  &        2.050     &     5.648  &         0.000        &        7.563    &       15.600     \\\\\n",
       "\\textbf{C(strike)[37]}  &       6.4713  &        2.059     &     3.142  &         0.002        &        2.435    &       10.508     \\\\\n",
       "\\textbf{C(strike)[39]}  &       8.3024  &        2.041     &     4.067  &         0.000        &        4.301    &       12.304     \\\\\n",
       "\\textbf{C(strike)[40]}  &      28.2648  &        2.093     &    13.505  &         0.000        &       24.163    &       32.367     \\\\\n",
       "\\textbf{C(strike)[42]}  &      18.9955  &        2.063     &     9.209  &         0.000        &       14.952    &       23.039     \\\\\n",
       "\\textbf{C(strike)[43]}  &      20.8110  &        1.869     &    11.132  &         0.000        &       17.147    &       24.475     \\\\\n",
       "\\textbf{C(strike)[47]}  &      13.6480  &        2.043     &     6.680  &         0.000        &        9.643    &       17.653     \\\\\n",
       "\\textbf{C(strike)[49]}  &      16.5403  &        2.040     &     8.109  &         0.000        &       12.542    &       20.538     \\\\\n",
       "\\textbf{C(strike)[57]}  &      15.4428  &        2.068     &     7.466  &         0.000        &       11.389    &       19.497     \\\\\n",
       "\\textbf{C(strike)[58]}  &      24.3538  &        1.870     &    13.022  &         0.000        &       20.688    &       28.019     \\\\\n",
       "\\textbf{C(strike)[59]}  &      16.7929  &        2.107     &     7.970  &         0.000        &       12.663    &       20.923     \\\\\n",
       "\\textbf{C(strike)[62]}  &      43.5397  &        2.014     &    21.622  &         0.000        &       39.593    &       47.487     \\\\\n",
       "\\textbf{C(strike)[71]}  &      69.1997  &        1.943     &    35.615  &         0.000        &       65.391    &       73.008     \\\\\n",
       "\\textbf{C(strike)[72]}  &      14.6578  &        2.079     &     7.052  &         0.000        &       10.584    &       18.732     \\\\\n",
       "\\textbf{C(strike)[75]}  &       5.0884  &        2.046     &     2.487  &         0.013        &        1.079    &        9.098     \\\\\n",
       "\\textbf{C(strike)[76]}  &      32.7346  &        1.975     &    16.575  &         0.000        &       28.864    &       36.605     \\\\\n",
       "\\textbf{C(strike)[83]}  &      26.7067  &        1.801     &    14.829  &         0.000        &       23.177    &       30.236     \\\\\n",
       "\\textbf{C(strike)[85]}  &      35.8376  &        1.851     &    19.358  &         0.000        &       32.209    &       39.466     \\\\\n",
       "\\textbf{C(strike)[87]}  &       9.4218  &        1.598     &     5.897  &         0.000        &        6.290    &       12.553     \\\\\n",
       "\\textbf{C(strike)[107]} &      51.0777  &        1.140     &    44.806  &         0.000        &       48.843    &       53.312     \\\\\n",
       "\\textbf{X\\_1}           &      -2.1785  &        2.583     &    -0.843  &         0.399        &       -7.241    &        2.884     \\\\\n",
       "\\textbf{X\\_2}           &      -0.3462  &        1.894     &    -0.183  &         0.855        &       -4.058    &        3.365     \\\\\n",
       "\\textbf{X\\_3}           &      -2.7391  &        2.319     &    -1.181  &         0.238        &       -7.284    &        1.806     \\\\\n",
       "\\textbf{X\\_4}           &      -2.3871  &        2.076     &    -1.150  &         0.250        &       -6.456    &        1.682     \\\\\n",
       "\\textbf{X\\_5}           &      -1.2667  &        0.732     &    -1.731  &         0.083        &       -2.701    &        0.167     \\\\\n",
       "\\textbf{X\\_7}           &       0.7400  &        0.905     &     0.817  &         0.414        &       -1.035    &        2.515     \\\\\n",
       "\\textbf{X\\_8}           &       0.0288  &        0.667     &     0.043  &         0.966        &       -1.279    &        1.337     \\\\\n",
       "\\textbf{X\\_9}           &       2.4144  &        1.297     &     1.862  &         0.063        &       -0.127    &        4.956     \\\\\n",
       "\\textbf{X\\_10}          &       3.1905  &        1.481     &     2.154  &         0.031        &        0.287    &        6.094     \\\\\n",
       "\\textbf{X\\_11}          &       4.4734  &        1.437     &     3.112  &         0.002        &        1.656    &        7.291     \\\\\n",
       "\\textbf{X\\_12}          &       4.8928  &        1.706     &     2.868  &         0.004        &        1.549    &        8.237     \\\\\n",
       "\\textbf{X\\_13}          &       4.3686  &        1.606     &     2.719  &         0.007        &        1.220    &        7.517     \\\\\n",
       "\\textbf{X\\_14}          &       6.9791  &        1.986     &     3.514  &         0.000        &        3.086    &       10.872     \\\\\n",
       "\\textbf{X\\_15}          &       5.3628  &        1.822     &     2.944  &         0.003        &        1.792    &        8.933     \\\\\n",
       "\\textbf{X\\_16}          &       6.8669  &        1.805     &     3.804  &         0.000        &        3.329    &       10.405     \\\\\n",
       "\\textbf{X\\_17}          &       8.4558  &        2.007     &     4.213  &         0.000        &        4.522    &       12.389     \\\\\n",
       "\\textbf{X\\_18}          &       8.0113  &        2.345     &     3.416  &         0.001        &        3.415    &       12.607     \\\\\n",
       "\\textbf{X\\_19}          &       6.6825  &        1.528     &     4.373  &         0.000        &        3.687    &        9.678     \\\\\n",
       "\\textbf{X\\_20}          &       7.4897  &        1.678     &     4.465  &         0.000        &        4.202    &       10.778     \\\\\n",
       "\\textbf{X\\_21}          &       8.3515  &        1.546     &     5.402  &         0.000        &        5.322    &       11.381     \\\\\n",
       "\\textbf{X\\_22}          &       9.0712  &        1.955     &     4.639  &         0.000        &        5.239    &       12.904     \\\\\n",
       "\\textbf{X\\_23}          &       9.1432  &        1.741     &     5.251  &         0.000        &        5.730    &       12.556     \\\\\n",
       "\\textbf{X\\_24}          &       9.5974  &        2.014     &     4.765  &         0.000        &        5.650    &       13.545     \\\\\n",
       "\\textbf{X\\_25}          &       9.4651  &        2.217     &     4.269  &         0.000        &        5.120    &       13.811     \\\\\n",
       "\\textbf{X\\_26}          &       9.5434  &        2.286     &     4.175  &         0.000        &        5.064    &       14.023     \\\\\n",
       "\\textbf{X\\_27}          &       9.2764  &        1.964     &     4.723  &         0.000        &        5.427    &       13.126     \\\\\n",
       "\\textbf{X\\_28}          &       9.8951  &        1.792     &     5.522  &         0.000        &        6.383    &       13.407     \\\\\n",
       "\\textbf{X\\_29}          &      12.0668  &        2.404     &     5.019  &         0.000        &        7.355    &       16.779     \\\\\n",
       "\\textbf{X\\_30}          &      11.5320  &        2.242     &     5.143  &         0.000        &        7.137    &       15.927     \\\\\n",
       "\\textbf{X\\_31}          &      11.1310  &        2.429     &     4.583  &         0.000        &        6.371    &       15.891     \\\\\n",
       "\\textbf{X\\_32}          &      10.7441  &        2.527     &     4.252  &         0.000        &        5.792    &       15.696     \\\\\n",
       "\\textbf{X\\_33}          &      10.6800  &        2.622     &     4.073  &         0.000        &        5.540    &       15.820     \\\\\n",
       "\\textbf{X\\_34}          &      11.2172  &        2.120     &     5.290  &         0.000        &        7.062    &       15.373     \\\\\n",
       "\\textbf{X\\_35}          &      12.0441  &        2.384     &     5.052  &         0.000        &        7.372    &       16.716     \\\\\n",
       "\\textbf{X\\_36}          &      11.7754  &        2.708     &     4.348  &         0.000        &        6.467    &       17.084     \\\\\n",
       "\\textbf{X\\_37}          &      12.8206  &        2.825     &     4.538  &         0.000        &        7.284    &       18.357     \\\\\n",
       "\\textbf{X\\_38}          &      13.1606  &        2.903     &     4.533  &         0.000        &        7.470    &       18.851     \\\\\n",
       "\\textbf{X\\_39}          &      13.1852  &        3.223     &     4.090  &         0.000        &        6.868    &       19.503     \\\\\n",
       "\\textbf{X\\_40}          &      12.7498  &        4.148     &     3.074  &         0.002        &        4.620    &       20.879     \\\\\n",
       "\\textbf{X\\_41}          &      12.1759  &        2.946     &     4.132  &         0.000        &        6.401    &       17.951     \\\\\n",
       "\\textbf{X\\_42}          &      13.0152  &        3.437     &     3.787  &         0.000        &        6.279    &       19.752     \\\\\n",
       "\\textbf{X\\_43}          &      12.8419  &        3.089     &     4.157  &         0.000        &        6.787    &       18.897     \\\\\n",
       "\\textbf{X\\_44}          &      13.9163  &        3.003     &     4.634  &         0.000        &        8.031    &       19.802     \\\\\n",
       "\\textbf{X\\_45}          &      13.7291  &        3.411     &     4.025  &         0.000        &        7.044    &       20.415     \\\\\n",
       "\\textbf{X\\_46}          &      13.5023  &        3.346     &     4.036  &         0.000        &        6.945    &       20.060     \\\\\n",
       "\\textbf{X\\_47}          &      14.2479  &        3.977     &     3.583  &         0.000        &        6.454    &       22.042     \\\\\n",
       "\\textbf{X\\_48}          &      15.0119  &        3.787     &     3.964  &         0.000        &        7.589    &       22.435     \\\\\n",
       "\\textbf{X\\_49}          &      15.2769  &        3.608     &     4.235  &         0.000        &        8.206    &       22.348     \\\\\n",
       "\\textbf{X\\_50}          &      15.1319  &        3.380     &     4.476  &         0.000        &        8.506    &       21.757     \\\\\n",
       "\\textbf{X\\_51}          &      14.6213  &        3.334     &     4.386  &         0.000        &        8.087    &       21.156     \\\\\n",
       "\\textbf{X\\_52}          &      15.8159  &        3.738     &     4.231  &         0.000        &        8.489    &       23.143     \\\\\n",
       "\\textbf{X\\_53}          &      15.4150  &        3.584     &     4.301  &         0.000        &        8.391    &       22.439     \\\\\n",
       "\\textbf{X\\_54}          &      14.8774  &        4.539     &     3.277  &         0.001        &        5.980    &       23.775     \\\\\n",
       "\\textbf{X\\_55}          &      15.5185  &        3.198     &     4.852  &         0.000        &        9.250    &       21.787     \\\\\n",
       "\\textbf{X\\_56}          &      15.8883  &        3.593     &     4.422  &         0.000        &        8.847    &       22.930     \\\\\n",
       "\\textbf{X\\_57}          &      16.1304  &        3.465     &     4.656  &         0.000        &        9.340    &       22.921     \\\\\n",
       "\\textbf{X\\_58}          &      16.0652  &        3.555     &     4.519  &         0.000        &        9.098    &       23.033     \\\\\n",
       "\\textbf{X\\_59}          &      15.2721  &        3.163     &     4.828  &         0.000        &        9.072    &       21.472     \\\\\n",
       "\\textbf{X\\_60}          &      16.6333  &        4.443     &     3.744  &         0.000        &        7.925    &       25.342     \\\\\n",
       "\\textbf{X\\_61}          &      15.0572  &        3.521     &     4.276  &         0.000        &        8.156    &       21.959     \\\\\n",
       "\\textbf{X\\_62}          &      15.9543  &        3.328     &     4.794  &         0.000        &        9.431    &       22.477     \\\\\n",
       "\\textbf{X\\_63}          &      14.6722  &        3.134     &     4.681  &         0.000        &        8.529    &       20.816     \\\\\n",
       "\\textbf{X\\_64}          &      16.1679  &        3.593     &     4.500  &         0.000        &        9.125    &       23.210     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 908982.476 & \\textbf{  Durbin-Watson:     } &      1.980    \\\\\n",
       "\\textbf{Prob(Omnibus):} &    0.000   & \\textbf{  Jarque-Bera (JB):  } & 47855945.075  \\\\\n",
       "\\textbf{Skew:}          &    5.105   & \\textbf{  Prob(JB):          } &       0.00    \\\\\n",
       "\\textbf{Kurtosis:}      &   37.190   & \\textbf{  Cond. No.          } &       40.6    \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors are robust to cluster correlation (cluster)"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:               distance   R-squared:                       0.030\n",
       "Model:                            OLS   Adj. R-squared:                  0.030\n",
       "Method:                 Least Squares   F-statistic:                       nan\n",
       "Date:                Wed, 21 May 2025   Prob (F-statistic):                nan\n",
       "Time:                        12:48:13   Log-Likelihood:            -5.1496e+06\n",
       "No. Observations:              902086   AIC:                         1.030e+07\n",
       "Df Residuals:                  901995   BIC:                         1.030e+07\n",
       "Df Model:                          90                                         \n",
       "Covariance Type:              cluster                                         \n",
       "==================================================================================\n",
       "                     coef    std err          z      P>|z|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------\n",
       "C(strike)[1]      15.2388      2.002      7.610      0.000      11.314      19.163\n",
       "C(strike)[3]      12.8538      2.046      6.282      0.000       8.844      16.864\n",
       "C(strike)[4]       8.2298      2.003      4.109      0.000       4.304      12.156\n",
       "C(strike)[6]      24.4013      2.086     11.700      0.000      20.314      28.489\n",
       "C(strike)[10]     16.1397      1.860      8.679      0.000      12.495      19.784\n",
       "C(strike)[14]     20.0794      1.990     10.092      0.000      16.180      23.979\n",
       "C(strike)[20]     26.1933      1.803     14.526      0.000      22.659      29.727\n",
       "C(strike)[31]     10.9714      2.161      5.078      0.000       6.736      15.206\n",
       "C(strike)[34]     11.5815      2.050      5.648      0.000       7.563      15.600\n",
       "C(strike)[37]      6.4713      2.059      3.142      0.002       2.435      10.508\n",
       "C(strike)[39]      8.3024      2.041      4.067      0.000       4.301      12.304\n",
       "C(strike)[40]     28.2648      2.093     13.505      0.000      24.163      32.367\n",
       "C(strike)[42]     18.9955      2.063      9.209      0.000      14.952      23.039\n",
       "C(strike)[43]     20.8110      1.869     11.132      0.000      17.147      24.475\n",
       "C(strike)[47]     13.6480      2.043      6.680      0.000       9.643      17.653\n",
       "C(strike)[49]     16.5403      2.040      8.109      0.000      12.542      20.538\n",
       "C(strike)[57]     15.4428      2.068      7.466      0.000      11.389      19.497\n",
       "C(strike)[58]     24.3538      1.870     13.022      0.000      20.688      28.019\n",
       "C(strike)[59]     16.7929      2.107      7.970      0.000      12.663      20.923\n",
       "C(strike)[62]     43.5397      2.014     21.622      0.000      39.593      47.487\n",
       "C(strike)[71]     69.1997      1.943     35.615      0.000      65.391      73.008\n",
       "C(strike)[72]     14.6578      2.079      7.052      0.000      10.584      18.732\n",
       "C(strike)[75]      5.0884      2.046      2.487      0.013       1.079       9.098\n",
       "C(strike)[76]     32.7346      1.975     16.575      0.000      28.864      36.605\n",
       "C(strike)[83]     26.7067      1.801     14.829      0.000      23.177      30.236\n",
       "C(strike)[85]     35.8376      1.851     19.358      0.000      32.209      39.466\n",
       "C(strike)[87]      9.4218      1.598      5.897      0.000       6.290      12.553\n",
       "C(strike)[107]    51.0777      1.140     44.806      0.000      48.843      53.312\n",
       "X_1               -2.1785      2.583     -0.843      0.399      -7.241       2.884\n",
       "X_2               -0.3462      1.894     -0.183      0.855      -4.058       3.365\n",
       "X_3               -2.7391      2.319     -1.181      0.238      -7.284       1.806\n",
       "X_4               -2.3871      2.076     -1.150      0.250      -6.456       1.682\n",
       "X_5               -1.2667      0.732     -1.731      0.083      -2.701       0.167\n",
       "X_7                0.7400      0.905      0.817      0.414      -1.035       2.515\n",
       "X_8                0.0288      0.667      0.043      0.966      -1.279       1.337\n",
       "X_9                2.4144      1.297      1.862      0.063      -0.127       4.956\n",
       "X_10               3.1905      1.481      2.154      0.031       0.287       6.094\n",
       "X_11               4.4734      1.437      3.112      0.002       1.656       7.291\n",
       "X_12               4.8928      1.706      2.868      0.004       1.549       8.237\n",
       "X_13               4.3686      1.606      2.719      0.007       1.220       7.517\n",
       "X_14               6.9791      1.986      3.514      0.000       3.086      10.872\n",
       "X_15               5.3628      1.822      2.944      0.003       1.792       8.933\n",
       "X_16               6.8669      1.805      3.804      0.000       3.329      10.405\n",
       "X_17               8.4558      2.007      4.213      0.000       4.522      12.389\n",
       "X_18               8.0113      2.345      3.416      0.001       3.415      12.607\n",
       "X_19               6.6825      1.528      4.373      0.000       3.687       9.678\n",
       "X_20               7.4897      1.678      4.465      0.000       4.202      10.778\n",
       "X_21               8.3515      1.546      5.402      0.000       5.322      11.381\n",
       "X_22               9.0712      1.955      4.639      0.000       5.239      12.904\n",
       "X_23               9.1432      1.741      5.251      0.000       5.730      12.556\n",
       "X_24               9.5974      2.014      4.765      0.000       5.650      13.545\n",
       "X_25               9.4651      2.217      4.269      0.000       5.120      13.811\n",
       "X_26               9.5434      2.286      4.175      0.000       5.064      14.023\n",
       "X_27               9.2764      1.964      4.723      0.000       5.427      13.126\n",
       "X_28               9.8951      1.792      5.522      0.000       6.383      13.407\n",
       "X_29              12.0668      2.404      5.019      0.000       7.355      16.779\n",
       "X_30              11.5320      2.242      5.143      0.000       7.137      15.927\n",
       "X_31              11.1310      2.429      4.583      0.000       6.371      15.891\n",
       "X_32              10.7441      2.527      4.252      0.000       5.792      15.696\n",
       "X_33              10.6800      2.622      4.073      0.000       5.540      15.820\n",
       "X_34              11.2172      2.120      5.290      0.000       7.062      15.373\n",
       "X_35              12.0441      2.384      5.052      0.000       7.372      16.716\n",
       "X_36              11.7754      2.708      4.348      0.000       6.467      17.084\n",
       "X_37              12.8206      2.825      4.538      0.000       7.284      18.357\n",
       "X_38              13.1606      2.903      4.533      0.000       7.470      18.851\n",
       "X_39              13.1852      3.223      4.090      0.000       6.868      19.503\n",
       "X_40              12.7498      4.148      3.074      0.002       4.620      20.879\n",
       "X_41              12.1759      2.946      4.132      0.000       6.401      17.951\n",
       "X_42              13.0152      3.437      3.787      0.000       6.279      19.752\n",
       "X_43              12.8419      3.089      4.157      0.000       6.787      18.897\n",
       "X_44              13.9163      3.003      4.634      0.000       8.031      19.802\n",
       "X_45              13.7291      3.411      4.025      0.000       7.044      20.415\n",
       "X_46              13.5023      3.346      4.036      0.000       6.945      20.060\n",
       "X_47              14.2479      3.977      3.583      0.000       6.454      22.042\n",
       "X_48              15.0119      3.787      3.964      0.000       7.589      22.435\n",
       "X_49              15.2769      3.608      4.235      0.000       8.206      22.348\n",
       "X_50              15.1319      3.380      4.476      0.000       8.506      21.757\n",
       "X_51              14.6213      3.334      4.386      0.000       8.087      21.156\n",
       "X_52              15.8159      3.738      4.231      0.000       8.489      23.143\n",
       "X_53              15.4150      3.584      4.301      0.000       8.391      22.439\n",
       "X_54              14.8774      4.539      3.277      0.001       5.980      23.775\n",
       "X_55              15.5185      3.198      4.852      0.000       9.250      21.787\n",
       "X_56              15.8883      3.593      4.422      0.000       8.847      22.930\n",
       "X_57              16.1304      3.465      4.656      0.000       9.340      22.921\n",
       "X_58              16.0652      3.555      4.519      0.000       9.098      23.033\n",
       "X_59              15.2721      3.163      4.828      0.000       9.072      21.472\n",
       "X_60              16.6333      4.443      3.744      0.000       7.925      25.342\n",
       "X_61              15.0572      3.521      4.276      0.000       8.156      21.959\n",
       "X_62              15.9543      3.328      4.794      0.000       9.431      22.477\n",
       "X_63              14.6722      3.134      4.681      0.000       8.529      20.816\n",
       "X_64              16.1679      3.593      4.500      0.000       9.125      23.210\n",
       "==============================================================================\n",
       "Omnibus:                   908982.476   Durbin-Watson:                   1.980\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):         47855945.075\n",
       "Skew:                           5.105   Prob(JB):                         0.00\n",
       "Kurtosis:                      37.190   Cond. No.                         40.6\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors are robust to cluster correlation (cluster)\n",
       "\"\"\""
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# run the event study regression for distance around strikes\n",
    "# 7 lags and 56 leads\n",
    "# time vars provide the parameters of interest\n",
    "# drop the day = -2 variable for difference in means and to avoid multicollinearity\n",
    "# use strike fixed effects and cluster SE by strike\n",
    "reg = sm.ols('distance ~ ' + var_form + ' + C(strike) - 1', \n",
    "             data=df_day).fit(cov_type='cluster',\n",
    "                          cov_kwds={'groups': np.array(df_day[['strike']])})\n",
    "\n",
    "\n",
    "\n",
    "# save parameters and confidence intervals for plotting\n",
    "conf_int = reg.conf_int()\n",
    "res_day = pd.DataFrame({\n",
    "    'params': reg.params,\n",
    "    'ci_l': conf_int[0],  # lower bound\n",
    "    'ci_h': conf_int[1],  # upper bound\n",
    "})\n",
    "\n",
    "# print regression summary\n",
    "reg.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>distance</td>     <th>  R-squared:         </th>  <td>   0.027</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.027</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>     nan</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 21 May 2025</td> <th>  Prob (F-statistic):</th>   <td>   nan</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>12:49:06</td>     <th>  Log-Likelihood:    </th> <td>-9.0777e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>1651419</td>     <th>  AIC:               </th>  <td>1.816e+07</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>1651328</td>     <th>  BIC:               </th>  <td>1.816e+07</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    90</td>      <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>       <td>cluster</td>     <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>           <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[5]</th>   <td>   25.9100</td> <td>    1.781</td> <td>   14.544</td> <td> 0.000</td> <td>   22.418</td> <td>   29.402</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[17]</th>  <td>   22.1922</td> <td>    1.636</td> <td>   13.563</td> <td> 0.000</td> <td>   18.985</td> <td>   25.399</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[19]</th>  <td>   24.1248</td> <td>    1.449</td> <td>   16.646</td> <td> 0.000</td> <td>   21.284</td> <td>   26.965</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[24]</th>  <td>   61.3319</td> <td>    1.708</td> <td>   35.905</td> <td> 0.000</td> <td>   57.984</td> <td>   64.680</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[26]</th>  <td>   30.0659</td> <td>    1.670</td> <td>   18.001</td> <td> 0.000</td> <td>   26.792</td> <td>   33.339</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[27]</th>  <td>   31.5124</td> <td>    1.654</td> <td>   19.048</td> <td> 0.000</td> <td>   28.270</td> <td>   34.755</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[32]</th>  <td>   20.6636</td> <td>    1.783</td> <td>   11.588</td> <td> 0.000</td> <td>   17.169</td> <td>   24.159</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[33]</th>  <td>   15.0765</td> <td>    1.754</td> <td>    8.597</td> <td> 0.000</td> <td>   11.639</td> <td>   18.514</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[35]</th>  <td>   11.9357</td> <td>    1.724</td> <td>    6.923</td> <td> 0.000</td> <td>    8.557</td> <td>   15.315</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[36]</th>  <td>   10.4573</td> <td>    1.753</td> <td>    5.966</td> <td> 0.000</td> <td>    7.022</td> <td>   13.893</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[41]</th>  <td>   10.9173</td> <td>    1.757</td> <td>    6.214</td> <td> 0.000</td> <td>    7.474</td> <td>   14.361</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[45]</th>  <td>   97.4002</td> <td>    1.609</td> <td>   60.532</td> <td> 0.000</td> <td>   94.246</td> <td>  100.554</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[48]</th>  <td>   25.1002</td> <td>    1.738</td> <td>   14.440</td> <td> 0.000</td> <td>   21.693</td> <td>   28.507</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[50]</th>  <td>   15.6415</td> <td>    1.787</td> <td>    8.751</td> <td> 0.000</td> <td>   12.138</td> <td>   19.145</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[51]</th>  <td>   61.5646</td> <td>    1.704</td> <td>   36.122</td> <td> 0.000</td> <td>   58.224</td> <td>   64.905</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[54]</th>  <td>   22.0575</td> <td>    1.785</td> <td>   12.354</td> <td> 0.000</td> <td>   18.558</td> <td>   25.557</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[56]</th>  <td>   29.0719</td> <td>    1.641</td> <td>   17.713</td> <td> 0.000</td> <td>   25.855</td> <td>   32.289</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[61]</th>  <td>   25.6772</td> <td>    1.701</td> <td>   15.095</td> <td> 0.000</td> <td>   22.343</td> <td>   29.011</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[64]</th>  <td>   22.4783</td> <td>    1.735</td> <td>   12.958</td> <td> 0.000</td> <td>   19.078</td> <td>   25.878</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[77]</th>  <td>   27.0110</td> <td>    1.781</td> <td>   15.167</td> <td> 0.000</td> <td>   23.521</td> <td>   30.501</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[78]</th>  <td>   22.8873</td> <td>    1.723</td> <td>   13.284</td> <td> 0.000</td> <td>   19.511</td> <td>   26.264</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[81]</th>  <td>   35.2491</td> <td>    1.698</td> <td>   20.758</td> <td> 0.000</td> <td>   31.921</td> <td>   38.577</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[92]</th>  <td>   21.3256</td> <td>    1.655</td> <td>   12.888</td> <td> 0.000</td> <td>   18.082</td> <td>   24.569</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[96]</th>  <td>    5.2868</td> <td>    1.770</td> <td>    2.987</td> <td> 0.003</td> <td>    1.818</td> <td>    8.756</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[97]</th>  <td>   29.6457</td> <td>    1.825</td> <td>   16.240</td> <td> 0.000</td> <td>   26.068</td> <td>   33.224</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[100]</th> <td>   93.6975</td> <td>    1.662</td> <td>   56.378</td> <td> 0.000</td> <td>   90.440</td> <td>   96.955</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[101]</th> <td>    6.4988</td> <td>    1.801</td> <td>    3.608</td> <td> 0.000</td> <td>    2.968</td> <td>   10.029</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[102]</th> <td>   26.3933</td> <td>    1.807</td> <td>   14.603</td> <td> 0.000</td> <td>   22.851</td> <td>   29.936</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_1</th>            <td>   -0.3806</td> <td>    0.705</td> <td>   -0.540</td> <td> 0.589</td> <td>   -1.763</td> <td>    1.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_2</th>            <td>    0.0233</td> <td>    0.753</td> <td>    0.031</td> <td> 0.975</td> <td>   -1.452</td> <td>    1.499</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_3</th>            <td>    0.2714</td> <td>    0.453</td> <td>    0.599</td> <td> 0.549</td> <td>   -0.616</td> <td>    1.159</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_4</th>            <td>   -0.1371</td> <td>    0.539</td> <td>   -0.254</td> <td> 0.799</td> <td>   -1.194</td> <td>    0.920</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_5</th>            <td>   -0.1653</td> <td>    0.730</td> <td>   -0.227</td> <td> 0.821</td> <td>   -1.595</td> <td>    1.265</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_7</th>            <td>    0.3749</td> <td>    0.679</td> <td>    0.552</td> <td> 0.581</td> <td>   -0.956</td> <td>    1.705</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_8</th>            <td>   -0.9439</td> <td>    0.793</td> <td>   -1.190</td> <td> 0.234</td> <td>   -2.499</td> <td>    0.611</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_9</th>            <td>    1.9488</td> <td>    0.733</td> <td>    2.658</td> <td> 0.008</td> <td>    0.512</td> <td>    3.386</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_10</th>           <td>    2.7079</td> <td>    0.655</td> <td>    4.134</td> <td> 0.000</td> <td>    1.424</td> <td>    3.992</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_11</th>           <td>    4.0359</td> <td>    0.927</td> <td>    4.352</td> <td> 0.000</td> <td>    2.218</td> <td>    5.854</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_12</th>           <td>    4.0584</td> <td>    0.891</td> <td>    4.555</td> <td> 0.000</td> <td>    2.312</td> <td>    5.805</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_13</th>           <td>    4.8430</td> <td>    1.143</td> <td>    4.237</td> <td> 0.000</td> <td>    2.603</td> <td>    7.083</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_14</th>           <td>    5.3790</td> <td>    1.029</td> <td>    5.228</td> <td> 0.000</td> <td>    3.362</td> <td>    7.396</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_15</th>           <td>    5.5429</td> <td>    1.182</td> <td>    4.688</td> <td> 0.000</td> <td>    3.225</td> <td>    7.860</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_16</th>           <td>    5.4832</td> <td>    1.077</td> <td>    5.091</td> <td> 0.000</td> <td>    3.372</td> <td>    7.594</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_17</th>           <td>    7.0818</td> <td>    1.749</td> <td>    4.049</td> <td> 0.000</td> <td>    3.654</td> <td>   10.510</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_18</th>           <td>    6.6040</td> <td>    1.426</td> <td>    4.631</td> <td> 0.000</td> <td>    3.809</td> <td>    9.399</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_19</th>           <td>    6.0626</td> <td>    1.366</td> <td>    4.439</td> <td> 0.000</td> <td>    3.386</td> <td>    8.739</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_20</th>           <td>    6.7058</td> <td>    1.663</td> <td>    4.032</td> <td> 0.000</td> <td>    3.446</td> <td>    9.966</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_21</th>           <td>    7.7823</td> <td>    1.634</td> <td>    4.762</td> <td> 0.000</td> <td>    4.579</td> <td>   10.986</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_22</th>           <td>    8.4954</td> <td>    1.477</td> <td>    5.753</td> <td> 0.000</td> <td>    5.601</td> <td>   11.390</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_23</th>           <td>    8.3103</td> <td>    1.591</td> <td>    5.222</td> <td> 0.000</td> <td>    5.191</td> <td>   11.429</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_24</th>           <td>    9.9115</td> <td>    2.159</td> <td>    4.591</td> <td> 0.000</td> <td>    5.680</td> <td>   14.143</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_25</th>           <td>    8.9593</td> <td>    1.732</td> <td>    5.174</td> <td> 0.000</td> <td>    5.565</td> <td>   12.353</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_26</th>           <td>    9.2140</td> <td>    1.989</td> <td>    4.632</td> <td> 0.000</td> <td>    5.315</td> <td>   13.112</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_27</th>           <td>    9.7194</td> <td>    2.270</td> <td>    4.282</td> <td> 0.000</td> <td>    5.271</td> <td>   14.168</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_28</th>           <td>   10.4209</td> <td>    2.063</td> <td>    5.051</td> <td> 0.000</td> <td>    6.378</td> <td>   14.464</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_29</th>           <td>   10.7716</td> <td>    1.896</td> <td>    5.681</td> <td> 0.000</td> <td>    7.055</td> <td>   14.488</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_30</th>           <td>   10.0631</td> <td>    1.792</td> <td>    5.617</td> <td> 0.000</td> <td>    6.552</td> <td>   13.575</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_31</th>           <td>    9.8080</td> <td>    1.816</td> <td>    5.401</td> <td> 0.000</td> <td>    6.249</td> <td>   13.367</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_32</th>           <td>    9.7438</td> <td>    2.087</td> <td>    4.669</td> <td> 0.000</td> <td>    5.654</td> <td>   13.834</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_33</th>           <td>    9.9414</td> <td>    1.984</td> <td>    5.010</td> <td> 0.000</td> <td>    6.052</td> <td>   13.831</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_34</th>           <td>   11.1443</td> <td>    2.611</td> <td>    4.268</td> <td> 0.000</td> <td>    6.026</td> <td>   16.262</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_35</th>           <td>   11.0480</td> <td>    2.296</td> <td>    4.812</td> <td> 0.000</td> <td>    6.548</td> <td>   15.548</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_36</th>           <td>   10.8206</td> <td>    2.071</td> <td>    5.225</td> <td> 0.000</td> <td>    6.762</td> <td>   14.880</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_37</th>           <td>   11.5703</td> <td>    2.472</td> <td>    4.681</td> <td> 0.000</td> <td>    6.726</td> <td>   16.415</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_38</th>           <td>   10.4345</td> <td>    2.445</td> <td>    4.267</td> <td> 0.000</td> <td>    5.642</td> <td>   15.227</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_39</th>           <td>   11.4620</td> <td>    2.623</td> <td>    4.369</td> <td> 0.000</td> <td>    6.320</td> <td>   16.604</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_40</th>           <td>   10.7045</td> <td>    2.640</td> <td>    4.055</td> <td> 0.000</td> <td>    5.531</td> <td>   15.878</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_41</th>           <td>   10.9140</td> <td>    2.781</td> <td>    3.925</td> <td> 0.000</td> <td>    5.463</td> <td>   16.365</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_42</th>           <td>   12.0014</td> <td>    2.862</td> <td>    4.194</td> <td> 0.000</td> <td>    6.392</td> <td>   17.611</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_43</th>           <td>   12.2257</td> <td>    2.727</td> <td>    4.484</td> <td> 0.000</td> <td>    6.882</td> <td>   17.570</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_44</th>           <td>   11.1440</td> <td>    2.320</td> <td>    4.804</td> <td> 0.000</td> <td>    6.597</td> <td>   15.691</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_45</th>           <td>   12.9638</td> <td>    3.072</td> <td>    4.220</td> <td> 0.000</td> <td>    6.943</td> <td>   18.985</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_46</th>           <td>   12.0376</td> <td>    2.730</td> <td>    4.409</td> <td> 0.000</td> <td>    6.686</td> <td>   17.389</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_47</th>           <td>   12.8018</td> <td>    2.968</td> <td>    4.313</td> <td> 0.000</td> <td>    6.984</td> <td>   18.619</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_48</th>           <td>   11.7670</td> <td>    2.892</td> <td>    4.068</td> <td> 0.000</td> <td>    6.098</td> <td>   17.436</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_49</th>           <td>   12.4061</td> <td>    2.781</td> <td>    4.461</td> <td> 0.000</td> <td>    6.956</td> <td>   17.856</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_50</th>           <td>   12.1319</td> <td>    2.667</td> <td>    4.549</td> <td> 0.000</td> <td>    6.905</td> <td>   17.359</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_51</th>           <td>   12.5744</td> <td>    2.654</td> <td>    4.738</td> <td> 0.000</td> <td>    7.372</td> <td>   17.777</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_52</th>           <td>   12.3357</td> <td>    2.984</td> <td>    4.134</td> <td> 0.000</td> <td>    6.488</td> <td>   18.183</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_53</th>           <td>   11.7747</td> <td>    2.936</td> <td>    4.011</td> <td> 0.000</td> <td>    6.021</td> <td>   17.529</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_54</th>           <td>   11.4905</td> <td>    2.965</td> <td>    3.875</td> <td> 0.000</td> <td>    5.679</td> <td>   17.302</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_55</th>           <td>   11.8260</td> <td>    3.029</td> <td>    3.904</td> <td> 0.000</td> <td>    5.889</td> <td>   17.763</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_56</th>           <td>   12.0872</td> <td>    2.924</td> <td>    4.134</td> <td> 0.000</td> <td>    6.356</td> <td>   17.818</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_57</th>           <td>   10.9731</td> <td>    2.611</td> <td>    4.202</td> <td> 0.000</td> <td>    5.855</td> <td>   16.091</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_58</th>           <td>   11.1549</td> <td>    2.826</td> <td>    3.948</td> <td> 0.000</td> <td>    5.617</td> <td>   16.693</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_59</th>           <td>   11.1964</td> <td>    3.055</td> <td>    3.665</td> <td> 0.000</td> <td>    5.209</td> <td>   17.184</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_60</th>           <td>   10.3596</td> <td>    3.014</td> <td>    3.437</td> <td> 0.001</td> <td>    4.452</td> <td>   16.267</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_61</th>           <td>    9.3772</td> <td>    3.065</td> <td>    3.059</td> <td> 0.002</td> <td>    3.370</td> <td>   15.385</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_62</th>           <td>   10.9794</td> <td>    3.259</td> <td>    3.369</td> <td> 0.001</td> <td>    4.591</td> <td>   17.368</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_63</th>           <td>   10.6316</td> <td>    2.976</td> <td>    3.573</td> <td> 0.000</td> <td>    4.799</td> <td>   16.464</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_64</th>           <td>   11.5919</td> <td>    3.192</td> <td>    3.632</td> <td> 0.000</td> <td>    5.336</td> <td>   17.848</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>1803579.026</td> <th>  Durbin-Watson:     </th>   <td>   1.970</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>   <td> 0.000</td>    <th>  Jarque-Bera (JB):  </th> <td>162246568.817</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>            <td> 5.619</td>    <th>  Prob(JB):          </th>   <td>    0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>        <td>50.240</td>    <th>  Cond. No.          </th>   <td>    39.0</td>   \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors are robust to cluster correlation (cluster)"
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &     distance     & \\textbf{  R-squared:         } &       0.027    \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &       0.027    \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &         nan    \\\\\n",
       "\\textbf{Date:}             & Wed, 21 May 2025 & \\textbf{  Prob (F-statistic):} &        nan     \\\\\n",
       "\\textbf{Time:}             &     12:49:06     & \\textbf{  Log-Likelihood:    } &  -9.0777e+06   \\\\\n",
       "\\textbf{No. Observations:} &     1651419      & \\textbf{  AIC:               } &   1.816e+07    \\\\\n",
       "\\textbf{Df Residuals:}     &     1651328      & \\textbf{  BIC:               } &   1.816e+07    \\\\\n",
       "\\textbf{Df Model:}         &          90      & \\textbf{                     } &                \\\\\n",
       "\\textbf{Covariance Type:}  &     cluster      & \\textbf{                     } &                \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                        & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{C(strike)[5]}   &      25.9100  &        1.781     &    14.544  &         0.000        &       22.418    &       29.402     \\\\\n",
       "\\textbf{C(strike)[17]}  &      22.1922  &        1.636     &    13.563  &         0.000        &       18.985    &       25.399     \\\\\n",
       "\\textbf{C(strike)[19]}  &      24.1248  &        1.449     &    16.646  &         0.000        &       21.284    &       26.965     \\\\\n",
       "\\textbf{C(strike)[24]}  &      61.3319  &        1.708     &    35.905  &         0.000        &       57.984    &       64.680     \\\\\n",
       "\\textbf{C(strike)[26]}  &      30.0659  &        1.670     &    18.001  &         0.000        &       26.792    &       33.339     \\\\\n",
       "\\textbf{C(strike)[27]}  &      31.5124  &        1.654     &    19.048  &         0.000        &       28.270    &       34.755     \\\\\n",
       "\\textbf{C(strike)[32]}  &      20.6636  &        1.783     &    11.588  &         0.000        &       17.169    &       24.159     \\\\\n",
       "\\textbf{C(strike)[33]}  &      15.0765  &        1.754     &     8.597  &         0.000        &       11.639    &       18.514     \\\\\n",
       "\\textbf{C(strike)[35]}  &      11.9357  &        1.724     &     6.923  &         0.000        &        8.557    &       15.315     \\\\\n",
       "\\textbf{C(strike)[36]}  &      10.4573  &        1.753     &     5.966  &         0.000        &        7.022    &       13.893     \\\\\n",
       "\\textbf{C(strike)[41]}  &      10.9173  &        1.757     &     6.214  &         0.000        &        7.474    &       14.361     \\\\\n",
       "\\textbf{C(strike)[45]}  &      97.4002  &        1.609     &    60.532  &         0.000        &       94.246    &      100.554     \\\\\n",
       "\\textbf{C(strike)[48]}  &      25.1002  &        1.738     &    14.440  &         0.000        &       21.693    &       28.507     \\\\\n",
       "\\textbf{C(strike)[50]}  &      15.6415  &        1.787     &     8.751  &         0.000        &       12.138    &       19.145     \\\\\n",
       "\\textbf{C(strike)[51]}  &      61.5646  &        1.704     &    36.122  &         0.000        &       58.224    &       64.905     \\\\\n",
       "\\textbf{C(strike)[54]}  &      22.0575  &        1.785     &    12.354  &         0.000        &       18.558    &       25.557     \\\\\n",
       "\\textbf{C(strike)[56]}  &      29.0719  &        1.641     &    17.713  &         0.000        &       25.855    &       32.289     \\\\\n",
       "\\textbf{C(strike)[61]}  &      25.6772  &        1.701     &    15.095  &         0.000        &       22.343    &       29.011     \\\\\n",
       "\\textbf{C(strike)[64]}  &      22.4783  &        1.735     &    12.958  &         0.000        &       19.078    &       25.878     \\\\\n",
       "\\textbf{C(strike)[77]}  &      27.0110  &        1.781     &    15.167  &         0.000        &       23.521    &       30.501     \\\\\n",
       "\\textbf{C(strike)[78]}  &      22.8873  &        1.723     &    13.284  &         0.000        &       19.511    &       26.264     \\\\\n",
       "\\textbf{C(strike)[81]}  &      35.2491  &        1.698     &    20.758  &         0.000        &       31.921    &       38.577     \\\\\n",
       "\\textbf{C(strike)[92]}  &      21.3256  &        1.655     &    12.888  &         0.000        &       18.082    &       24.569     \\\\\n",
       "\\textbf{C(strike)[96]}  &       5.2868  &        1.770     &     2.987  &         0.003        &        1.818    &        8.756     \\\\\n",
       "\\textbf{C(strike)[97]}  &      29.6457  &        1.825     &    16.240  &         0.000        &       26.068    &       33.224     \\\\\n",
       "\\textbf{C(strike)[100]} &      93.6975  &        1.662     &    56.378  &         0.000        &       90.440    &       96.955     \\\\\n",
       "\\textbf{C(strike)[101]} &       6.4988  &        1.801     &     3.608  &         0.000        &        2.968    &       10.029     \\\\\n",
       "\\textbf{C(strike)[102]} &      26.3933  &        1.807     &    14.603  &         0.000        &       22.851    &       29.936     \\\\\n",
       "\\textbf{X\\_1}           &      -0.3806  &        0.705     &    -0.540  &         0.589        &       -1.763    &        1.002     \\\\\n",
       "\\textbf{X\\_2}           &       0.0233  &        0.753     &     0.031  &         0.975        &       -1.452    &        1.499     \\\\\n",
       "\\textbf{X\\_3}           &       0.2714  &        0.453     &     0.599  &         0.549        &       -0.616    &        1.159     \\\\\n",
       "\\textbf{X\\_4}           &      -0.1371  &        0.539     &    -0.254  &         0.799        &       -1.194    &        0.920     \\\\\n",
       "\\textbf{X\\_5}           &      -0.1653  &        0.730     &    -0.227  &         0.821        &       -1.595    &        1.265     \\\\\n",
       "\\textbf{X\\_7}           &       0.3749  &        0.679     &     0.552  &         0.581        &       -0.956    &        1.705     \\\\\n",
       "\\textbf{X\\_8}           &      -0.9439  &        0.793     &    -1.190  &         0.234        &       -2.499    &        0.611     \\\\\n",
       "\\textbf{X\\_9}           &       1.9488  &        0.733     &     2.658  &         0.008        &        0.512    &        3.386     \\\\\n",
       "\\textbf{X\\_10}          &       2.7079  &        0.655     &     4.134  &         0.000        &        1.424    &        3.992     \\\\\n",
       "\\textbf{X\\_11}          &       4.0359  &        0.927     &     4.352  &         0.000        &        2.218    &        5.854     \\\\\n",
       "\\textbf{X\\_12}          &       4.0584  &        0.891     &     4.555  &         0.000        &        2.312    &        5.805     \\\\\n",
       "\\textbf{X\\_13}          &       4.8430  &        1.143     &     4.237  &         0.000        &        2.603    &        7.083     \\\\\n",
       "\\textbf{X\\_14}          &       5.3790  &        1.029     &     5.228  &         0.000        &        3.362    &        7.396     \\\\\n",
       "\\textbf{X\\_15}          &       5.5429  &        1.182     &     4.688  &         0.000        &        3.225    &        7.860     \\\\\n",
       "\\textbf{X\\_16}          &       5.4832  &        1.077     &     5.091  &         0.000        &        3.372    &        7.594     \\\\\n",
       "\\textbf{X\\_17}          &       7.0818  &        1.749     &     4.049  &         0.000        &        3.654    &       10.510     \\\\\n",
       "\\textbf{X\\_18}          &       6.6040  &        1.426     &     4.631  &         0.000        &        3.809    &        9.399     \\\\\n",
       "\\textbf{X\\_19}          &       6.0626  &        1.366     &     4.439  &         0.000        &        3.386    &        8.739     \\\\\n",
       "\\textbf{X\\_20}          &       6.7058  &        1.663     &     4.032  &         0.000        &        3.446    &        9.966     \\\\\n",
       "\\textbf{X\\_21}          &       7.7823  &        1.634     &     4.762  &         0.000        &        4.579    &       10.986     \\\\\n",
       "\\textbf{X\\_22}          &       8.4954  &        1.477     &     5.753  &         0.000        &        5.601    &       11.390     \\\\\n",
       "\\textbf{X\\_23}          &       8.3103  &        1.591     &     5.222  &         0.000        &        5.191    &       11.429     \\\\\n",
       "\\textbf{X\\_24}          &       9.9115  &        2.159     &     4.591  &         0.000        &        5.680    &       14.143     \\\\\n",
       "\\textbf{X\\_25}          &       8.9593  &        1.732     &     5.174  &         0.000        &        5.565    &       12.353     \\\\\n",
       "\\textbf{X\\_26}          &       9.2140  &        1.989     &     4.632  &         0.000        &        5.315    &       13.112     \\\\\n",
       "\\textbf{X\\_27}          &       9.7194  &        2.270     &     4.282  &         0.000        &        5.271    &       14.168     \\\\\n",
       "\\textbf{X\\_28}          &      10.4209  &        2.063     &     5.051  &         0.000        &        6.378    &       14.464     \\\\\n",
       "\\textbf{X\\_29}          &      10.7716  &        1.896     &     5.681  &         0.000        &        7.055    &       14.488     \\\\\n",
       "\\textbf{X\\_30}          &      10.0631  &        1.792     &     5.617  &         0.000        &        6.552    &       13.575     \\\\\n",
       "\\textbf{X\\_31}          &       9.8080  &        1.816     &     5.401  &         0.000        &        6.249    &       13.367     \\\\\n",
       "\\textbf{X\\_32}          &       9.7438  &        2.087     &     4.669  &         0.000        &        5.654    &       13.834     \\\\\n",
       "\\textbf{X\\_33}          &       9.9414  &        1.984     &     5.010  &         0.000        &        6.052    &       13.831     \\\\\n",
       "\\textbf{X\\_34}          &      11.1443  &        2.611     &     4.268  &         0.000        &        6.026    &       16.262     \\\\\n",
       "\\textbf{X\\_35}          &      11.0480  &        2.296     &     4.812  &         0.000        &        6.548    &       15.548     \\\\\n",
       "\\textbf{X\\_36}          &      10.8206  &        2.071     &     5.225  &         0.000        &        6.762    &       14.880     \\\\\n",
       "\\textbf{X\\_37}          &      11.5703  &        2.472     &     4.681  &         0.000        &        6.726    &       16.415     \\\\\n",
       "\\textbf{X\\_38}          &      10.4345  &        2.445     &     4.267  &         0.000        &        5.642    &       15.227     \\\\\n",
       "\\textbf{X\\_39}          &      11.4620  &        2.623     &     4.369  &         0.000        &        6.320    &       16.604     \\\\\n",
       "\\textbf{X\\_40}          &      10.7045  &        2.640     &     4.055  &         0.000        &        5.531    &       15.878     \\\\\n",
       "\\textbf{X\\_41}          &      10.9140  &        2.781     &     3.925  &         0.000        &        5.463    &       16.365     \\\\\n",
       "\\textbf{X\\_42}          &      12.0014  &        2.862     &     4.194  &         0.000        &        6.392    &       17.611     \\\\\n",
       "\\textbf{X\\_43}          &      12.2257  &        2.727     &     4.484  &         0.000        &        6.882    &       17.570     \\\\\n",
       "\\textbf{X\\_44}          &      11.1440  &        2.320     &     4.804  &         0.000        &        6.597    &       15.691     \\\\\n",
       "\\textbf{X\\_45}          &      12.9638  &        3.072     &     4.220  &         0.000        &        6.943    &       18.985     \\\\\n",
       "\\textbf{X\\_46}          &      12.0376  &        2.730     &     4.409  &         0.000        &        6.686    &       17.389     \\\\\n",
       "\\textbf{X\\_47}          &      12.8018  &        2.968     &     4.313  &         0.000        &        6.984    &       18.619     \\\\\n",
       "\\textbf{X\\_48}          &      11.7670  &        2.892     &     4.068  &         0.000        &        6.098    &       17.436     \\\\\n",
       "\\textbf{X\\_49}          &      12.4061  &        2.781     &     4.461  &         0.000        &        6.956    &       17.856     \\\\\n",
       "\\textbf{X\\_50}          &      12.1319  &        2.667     &     4.549  &         0.000        &        6.905    &       17.359     \\\\\n",
       "\\textbf{X\\_51}          &      12.5744  &        2.654     &     4.738  &         0.000        &        7.372    &       17.777     \\\\\n",
       "\\textbf{X\\_52}          &      12.3357  &        2.984     &     4.134  &         0.000        &        6.488    &       18.183     \\\\\n",
       "\\textbf{X\\_53}          &      11.7747  &        2.936     &     4.011  &         0.000        &        6.021    &       17.529     \\\\\n",
       "\\textbf{X\\_54}          &      11.4905  &        2.965     &     3.875  &         0.000        &        5.679    &       17.302     \\\\\n",
       "\\textbf{X\\_55}          &      11.8260  &        3.029     &     3.904  &         0.000        &        5.889    &       17.763     \\\\\n",
       "\\textbf{X\\_56}          &      12.0872  &        2.924     &     4.134  &         0.000        &        6.356    &       17.818     \\\\\n",
       "\\textbf{X\\_57}          &      10.9731  &        2.611     &     4.202  &         0.000        &        5.855    &       16.091     \\\\\n",
       "\\textbf{X\\_58}          &      11.1549  &        2.826     &     3.948  &         0.000        &        5.617    &       16.693     \\\\\n",
       "\\textbf{X\\_59}          &      11.1964  &        3.055     &     3.665  &         0.000        &        5.209    &       17.184     \\\\\n",
       "\\textbf{X\\_60}          &      10.3596  &        3.014     &     3.437  &         0.001        &        4.452    &       16.267     \\\\\n",
       "\\textbf{X\\_61}          &       9.3772  &        3.065     &     3.059  &         0.002        &        3.370    &       15.385     \\\\\n",
       "\\textbf{X\\_62}          &      10.9794  &        3.259     &     3.369  &         0.001        &        4.591    &       17.368     \\\\\n",
       "\\textbf{X\\_63}          &      10.6316  &        2.976     &     3.573  &         0.000        &        4.799    &       16.464     \\\\\n",
       "\\textbf{X\\_64}          &      11.5919  &        3.192     &     3.632  &         0.000        &        5.336    &       17.848     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 1803579.026 & \\textbf{  Durbin-Watson:     } &       1.970    \\\\\n",
       "\\textbf{Prob(Omnibus):} &     0.000   & \\textbf{  Jarque-Bera (JB):  } & 162246568.817  \\\\\n",
       "\\textbf{Skew:}          &     5.619   & \\textbf{  Prob(JB):          } &        0.00    \\\\\n",
       "\\textbf{Kurtosis:}      &    50.240   & \\textbf{  Cond. No.          } &        39.0    \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors are robust to cluster correlation (cluster)"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:               distance   R-squared:                       0.027\n",
       "Model:                            OLS   Adj. R-squared:                  0.027\n",
       "Method:                 Least Squares   F-statistic:                       nan\n",
       "Date:                Wed, 21 May 2025   Prob (F-statistic):                nan\n",
       "Time:                        12:49:06   Log-Likelihood:            -9.0777e+06\n",
       "No. Observations:             1651419   AIC:                         1.816e+07\n",
       "Df Residuals:                 1651328   BIC:                         1.816e+07\n",
       "Df Model:                          90                                         \n",
       "Covariance Type:              cluster                                         \n",
       "==================================================================================\n",
       "                     coef    std err          z      P>|z|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------\n",
       "C(strike)[5]      25.9100      1.781     14.544      0.000      22.418      29.402\n",
       "C(strike)[17]     22.1922      1.636     13.563      0.000      18.985      25.399\n",
       "C(strike)[19]     24.1248      1.449     16.646      0.000      21.284      26.965\n",
       "C(strike)[24]     61.3319      1.708     35.905      0.000      57.984      64.680\n",
       "C(strike)[26]     30.0659      1.670     18.001      0.000      26.792      33.339\n",
       "C(strike)[27]     31.5124      1.654     19.048      0.000      28.270      34.755\n",
       "C(strike)[32]     20.6636      1.783     11.588      0.000      17.169      24.159\n",
       "C(strike)[33]     15.0765      1.754      8.597      0.000      11.639      18.514\n",
       "C(strike)[35]     11.9357      1.724      6.923      0.000       8.557      15.315\n",
       "C(strike)[36]     10.4573      1.753      5.966      0.000       7.022      13.893\n",
       "C(strike)[41]     10.9173      1.757      6.214      0.000       7.474      14.361\n",
       "C(strike)[45]     97.4002      1.609     60.532      0.000      94.246     100.554\n",
       "C(strike)[48]     25.1002      1.738     14.440      0.000      21.693      28.507\n",
       "C(strike)[50]     15.6415      1.787      8.751      0.000      12.138      19.145\n",
       "C(strike)[51]     61.5646      1.704     36.122      0.000      58.224      64.905\n",
       "C(strike)[54]     22.0575      1.785     12.354      0.000      18.558      25.557\n",
       "C(strike)[56]     29.0719      1.641     17.713      0.000      25.855      32.289\n",
       "C(strike)[61]     25.6772      1.701     15.095      0.000      22.343      29.011\n",
       "C(strike)[64]     22.4783      1.735     12.958      0.000      19.078      25.878\n",
       "C(strike)[77]     27.0110      1.781     15.167      0.000      23.521      30.501\n",
       "C(strike)[78]     22.8873      1.723     13.284      0.000      19.511      26.264\n",
       "C(strike)[81]     35.2491      1.698     20.758      0.000      31.921      38.577\n",
       "C(strike)[92]     21.3256      1.655     12.888      0.000      18.082      24.569\n",
       "C(strike)[96]      5.2868      1.770      2.987      0.003       1.818       8.756\n",
       "C(strike)[97]     29.6457      1.825     16.240      0.000      26.068      33.224\n",
       "C(strike)[100]    93.6975      1.662     56.378      0.000      90.440      96.955\n",
       "C(strike)[101]     6.4988      1.801      3.608      0.000       2.968      10.029\n",
       "C(strike)[102]    26.3933      1.807     14.603      0.000      22.851      29.936\n",
       "X_1               -0.3806      0.705     -0.540      0.589      -1.763       1.002\n",
       "X_2                0.0233      0.753      0.031      0.975      -1.452       1.499\n",
       "X_3                0.2714      0.453      0.599      0.549      -0.616       1.159\n",
       "X_4               -0.1371      0.539     -0.254      0.799      -1.194       0.920\n",
       "X_5               -0.1653      0.730     -0.227      0.821      -1.595       1.265\n",
       "X_7                0.3749      0.679      0.552      0.581      -0.956       1.705\n",
       "X_8               -0.9439      0.793     -1.190      0.234      -2.499       0.611\n",
       "X_9                1.9488      0.733      2.658      0.008       0.512       3.386\n",
       "X_10               2.7079      0.655      4.134      0.000       1.424       3.992\n",
       "X_11               4.0359      0.927      4.352      0.000       2.218       5.854\n",
       "X_12               4.0584      0.891      4.555      0.000       2.312       5.805\n",
       "X_13               4.8430      1.143      4.237      0.000       2.603       7.083\n",
       "X_14               5.3790      1.029      5.228      0.000       3.362       7.396\n",
       "X_15               5.5429      1.182      4.688      0.000       3.225       7.860\n",
       "X_16               5.4832      1.077      5.091      0.000       3.372       7.594\n",
       "X_17               7.0818      1.749      4.049      0.000       3.654      10.510\n",
       "X_18               6.6040      1.426      4.631      0.000       3.809       9.399\n",
       "X_19               6.0626      1.366      4.439      0.000       3.386       8.739\n",
       "X_20               6.7058      1.663      4.032      0.000       3.446       9.966\n",
       "X_21               7.7823      1.634      4.762      0.000       4.579      10.986\n",
       "X_22               8.4954      1.477      5.753      0.000       5.601      11.390\n",
       "X_23               8.3103      1.591      5.222      0.000       5.191      11.429\n",
       "X_24               9.9115      2.159      4.591      0.000       5.680      14.143\n",
       "X_25               8.9593      1.732      5.174      0.000       5.565      12.353\n",
       "X_26               9.2140      1.989      4.632      0.000       5.315      13.112\n",
       "X_27               9.7194      2.270      4.282      0.000       5.271      14.168\n",
       "X_28              10.4209      2.063      5.051      0.000       6.378      14.464\n",
       "X_29              10.7716      1.896      5.681      0.000       7.055      14.488\n",
       "X_30              10.0631      1.792      5.617      0.000       6.552      13.575\n",
       "X_31               9.8080      1.816      5.401      0.000       6.249      13.367\n",
       "X_32               9.7438      2.087      4.669      0.000       5.654      13.834\n",
       "X_33               9.9414      1.984      5.010      0.000       6.052      13.831\n",
       "X_34              11.1443      2.611      4.268      0.000       6.026      16.262\n",
       "X_35              11.0480      2.296      4.812      0.000       6.548      15.548\n",
       "X_36              10.8206      2.071      5.225      0.000       6.762      14.880\n",
       "X_37              11.5703      2.472      4.681      0.000       6.726      16.415\n",
       "X_38              10.4345      2.445      4.267      0.000       5.642      15.227\n",
       "X_39              11.4620      2.623      4.369      0.000       6.320      16.604\n",
       "X_40              10.7045      2.640      4.055      0.000       5.531      15.878\n",
       "X_41              10.9140      2.781      3.925      0.000       5.463      16.365\n",
       "X_42              12.0014      2.862      4.194      0.000       6.392      17.611\n",
       "X_43              12.2257      2.727      4.484      0.000       6.882      17.570\n",
       "X_44              11.1440      2.320      4.804      0.000       6.597      15.691\n",
       "X_45              12.9638      3.072      4.220      0.000       6.943      18.985\n",
       "X_46              12.0376      2.730      4.409      0.000       6.686      17.389\n",
       "X_47              12.8018      2.968      4.313      0.000       6.984      18.619\n",
       "X_48              11.7670      2.892      4.068      0.000       6.098      17.436\n",
       "X_49              12.4061      2.781      4.461      0.000       6.956      17.856\n",
       "X_50              12.1319      2.667      4.549      0.000       6.905      17.359\n",
       "X_51              12.5744      2.654      4.738      0.000       7.372      17.777\n",
       "X_52              12.3357      2.984      4.134      0.000       6.488      18.183\n",
       "X_53              11.7747      2.936      4.011      0.000       6.021      17.529\n",
       "X_54              11.4905      2.965      3.875      0.000       5.679      17.302\n",
       "X_55              11.8260      3.029      3.904      0.000       5.889      17.763\n",
       "X_56              12.0872      2.924      4.134      0.000       6.356      17.818\n",
       "X_57              10.9731      2.611      4.202      0.000       5.855      16.091\n",
       "X_58              11.1549      2.826      3.948      0.000       5.617      16.693\n",
       "X_59              11.1964      3.055      3.665      0.000       5.209      17.184\n",
       "X_60              10.3596      3.014      3.437      0.001       4.452      16.267\n",
       "X_61               9.3772      3.065      3.059      0.002       3.370      15.385\n",
       "X_62              10.9794      3.259      3.369      0.001       4.591      17.368\n",
       "X_63              10.6316      2.976      3.573      0.000       4.799      16.464\n",
       "X_64              11.5919      3.192      3.632      0.000       5.336      17.848\n",
       "==============================================================================\n",
       "Omnibus:                  1803579.026   Durbin-Watson:                   1.970\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):        162246568.817\n",
       "Skew:                           5.619   Prob(JB):                         0.00\n",
       "Kurtosis:                      50.240   Cond. No.                         39.0\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors are robust to cluster correlation (cluster)\n",
       "\"\"\""
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# run the event study regression for distance around strikes\n",
    "# 7 lags and 56 leads\n",
    "# time vars provide the parameters of interest\n",
    "# drop the day = -2 variable for difference in means and to avoid multicollinearity\n",
    "# use strike fixed effects and cluster SE by strike\n",
    "reg = sm.ols('distance ~ ' + var_form + ' + C(strike) - 1', \n",
    "             data=df_evening).fit(cov_type='cluster',\n",
    "                          cov_kwds={'groups': np.array(df_evening[['strike']])})\n",
    "\n",
    "\n",
    "\n",
    "# save parameters and confidence intervals for plotting\n",
    "conf_int = reg.conf_int()\n",
    "res_evening = pd.DataFrame({\n",
    "    'params': reg.params,\n",
    "    'ci_l': conf_int[0],  # lower bound\n",
    "    'ci_h': conf_int[1],  # upper bound\n",
    "})\n",
    "\n",
    "# print regression summary\n",
    "reg.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "event_res_morning = res_morning[res_morning.index.to_series().str.startswith(\"X_\")].copy()\n",
    "event_res_morning = event_res_morning.reset_index().rename(columns={'index': 'varname'})\n",
    "event_res_morning['day'] = event_res_morning['varname'].str.extract(r'X_(\\d+)').astype(float).astype('Int64') - 8\n",
    "\n",
    "event_res_day = res_day[res_day.index.to_series().str.startswith(\"X_\")].copy()\n",
    "event_res_day = event_res_day.reset_index().rename(columns={'index': 'varname'})\n",
    "event_res_day['day'] = event_res_day['varname'].str.extract(r'X_(\\d+)').astype(float).astype('Int64') - 8\n",
    "\n",
    "event_res_evening = res_evening[res_evening.index.to_series().str.startswith(\"X_\")].copy()\n",
    "event_res_evening = event_res_evening.reset_index().rename(columns={'index': 'varname'})\n",
    "event_res_evening['day'] = event_res_evening['varname'].str.extract(r'X_(\\d+)').astype(float).astype('Int64') - 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_three_group(event_res_morning, event_res_day, event_res_evening,\n",
    "                       label1='Morning (0:00 - 8:00)', label2='Day (8:01 - 16:00)', label3='Evening (16:01 - 23:59)',\n",
    "                       color1='C0', color2='C1', color3='C2',\n",
    "                       ylabel='Distance (km)',\n",
    "                       outfile='figures/figureX_distance_time_of_day.pdf',\n",
    "                       ylim=[-7,34], xlim=[-7,56])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure S12B: Distance Results with High-Ranking Militant Subgroups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "# subset distance df by high ranking and low ranking militants\n",
    "df_high_ranking = df[df['high_ranking'] == 1]\n",
    "df_low_ranking = df[df['high_ranking'] == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>distance</td>     <th>  R-squared:         </th>  <td>   0.021</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.021</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>     nan</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 21 May 2025</td> <th>  Prob (F-statistic):</th>   <td>   nan</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>12:50:37</td>     <th>  Log-Likelihood:    </th> <td>-6.0681e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>1122466</td>     <th>  AIC:               </th>  <td>1.214e+07</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>1122386</td>     <th>  BIC:               </th>  <td>1.214e+07</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    79</td>      <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>       <td>cluster</td>     <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>           <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[8]</th>   <td>    6.3997</td> <td>    1.581</td> <td>    4.048</td> <td> 0.000</td> <td>    3.301</td> <td>    9.498</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[9]</th>   <td>   11.1285</td> <td>    1.471</td> <td>    7.565</td> <td> 0.000</td> <td>    8.245</td> <td>   14.011</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[27]</th>  <td>   32.3514</td> <td>    1.487</td> <td>   21.751</td> <td> 0.000</td> <td>   29.436</td> <td>   35.267</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[32]</th>  <td>   21.5649</td> <td>    1.578</td> <td>   13.666</td> <td> 0.000</td> <td>   18.472</td> <td>   24.658</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[36]</th>  <td>   11.3384</td> <td>    1.555</td> <td>    7.291</td> <td> 0.000</td> <td>    8.290</td> <td>   14.386</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[42]</th>  <td>   21.3559</td> <td>    1.564</td> <td>   13.659</td> <td> 0.000</td> <td>   18.292</td> <td>   24.420</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[51]</th>  <td>   62.4166</td> <td>    1.522</td> <td>   41.001</td> <td> 0.000</td> <td>   59.433</td> <td>   65.400</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[56]</th>  <td>   29.9473</td> <td>    1.442</td> <td>   20.769</td> <td> 0.000</td> <td>   27.121</td> <td>   32.773</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[59]</th>  <td>   19.2128</td> <td>    1.589</td> <td>   12.091</td> <td> 0.000</td> <td>   16.098</td> <td>   22.327</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[60]</th>  <td>   24.5247</td> <td>    1.394</td> <td>   17.594</td> <td> 0.000</td> <td>   21.793</td> <td>   27.257</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[68]</th>  <td>   24.8234</td> <td>    1.388</td> <td>   17.889</td> <td> 0.000</td> <td>   22.104</td> <td>   27.543</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[70]</th>  <td>   41.0733</td> <td>    1.532</td> <td>   26.807</td> <td> 0.000</td> <td>   38.070</td> <td>   44.076</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[75]</th>  <td>    7.3207</td> <td>    1.567</td> <td>    4.671</td> <td> 0.000</td> <td>    4.249</td> <td>   10.393</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[90]</th>  <td>    7.3334</td> <td>    1.655</td> <td>    4.431</td> <td> 0.000</td> <td>    4.090</td> <td>   10.577</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[92]</th>  <td>   22.1585</td> <td>    1.464</td> <td>   15.132</td> <td> 0.000</td> <td>   19.288</td> <td>   25.028</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[96]</th>  <td>    6.2069</td> <td>    1.565</td> <td>    3.965</td> <td> 0.000</td> <td>    3.139</td> <td>    9.275</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[100]</th> <td>   94.5132</td> <td>    1.504</td> <td>   62.830</td> <td> 0.000</td> <td>   91.565</td> <td>   97.461</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_1</th>            <td>    0.0860</td> <td>    0.818</td> <td>    0.105</td> <td> 0.916</td> <td>   -1.517</td> <td>    1.689</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_2</th>            <td>    0.1569</td> <td>    0.460</td> <td>    0.341</td> <td> 0.733</td> <td>   -0.744</td> <td>    1.058</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_3</th>            <td>   -0.1503</td> <td>    0.539</td> <td>   -0.279</td> <td> 0.780</td> <td>   -1.207</td> <td>    0.906</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_4</th>            <td>   -0.4466</td> <td>    0.535</td> <td>   -0.835</td> <td> 0.404</td> <td>   -1.495</td> <td>    0.602</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_5</th>            <td>   -0.3552</td> <td>    0.422</td> <td>   -0.843</td> <td> 0.399</td> <td>   -1.182</td> <td>    0.471</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_7</th>            <td>    0.2063</td> <td>    0.391</td> <td>    0.527</td> <td> 0.598</td> <td>   -0.561</td> <td>    0.973</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_8</th>            <td>    0.4735</td> <td>    0.963</td> <td>    0.491</td> <td> 0.623</td> <td>   -1.415</td> <td>    2.362</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_9</th>            <td>    1.3909</td> <td>    0.567</td> <td>    2.452</td> <td> 0.014</td> <td>    0.279</td> <td>    2.503</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_10</th>           <td>    2.2659</td> <td>    0.692</td> <td>    3.275</td> <td> 0.001</td> <td>    0.910</td> <td>    3.622</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_11</th>           <td>    3.0489</td> <td>    1.078</td> <td>    2.829</td> <td> 0.005</td> <td>    0.936</td> <td>    5.161</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_12</th>           <td>    2.9647</td> <td>    0.974</td> <td>    3.044</td> <td> 0.002</td> <td>    1.056</td> <td>    4.874</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_13</th>           <td>    3.9978</td> <td>    1.057</td> <td>    3.783</td> <td> 0.000</td> <td>    1.926</td> <td>    6.069</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_14</th>           <td>    4.4349</td> <td>    1.199</td> <td>    3.699</td> <td> 0.000</td> <td>    2.085</td> <td>    6.784</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_15</th>           <td>    4.7951</td> <td>    1.442</td> <td>    3.325</td> <td> 0.001</td> <td>    1.969</td> <td>    7.621</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_16</th>           <td>    4.6474</td> <td>    1.119</td> <td>    4.151</td> <td> 0.000</td> <td>    2.453</td> <td>    6.842</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_17</th>           <td>    5.4441</td> <td>    1.632</td> <td>    3.337</td> <td> 0.001</td> <td>    2.246</td> <td>    8.642</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_18</th>           <td>    5.0650</td> <td>    1.578</td> <td>    3.209</td> <td> 0.001</td> <td>    1.971</td> <td>    8.159</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_19</th>           <td>    4.9594</td> <td>    1.217</td> <td>    4.076</td> <td> 0.000</td> <td>    2.575</td> <td>    7.344</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_20</th>           <td>    5.9651</td> <td>    1.636</td> <td>    3.645</td> <td> 0.000</td> <td>    2.758</td> <td>    9.172</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_21</th>           <td>    6.0250</td> <td>    0.995</td> <td>    6.056</td> <td> 0.000</td> <td>    4.075</td> <td>    7.975</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_22</th>           <td>    6.6108</td> <td>    1.178</td> <td>    5.614</td> <td> 0.000</td> <td>    4.303</td> <td>    8.919</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_23</th>           <td>    7.5220</td> <td>    1.477</td> <td>    5.094</td> <td> 0.000</td> <td>    4.628</td> <td>   10.416</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_24</th>           <td>    7.6613</td> <td>    1.681</td> <td>    4.557</td> <td> 0.000</td> <td>    4.366</td> <td>   10.956</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_25</th>           <td>    7.7675</td> <td>    1.752</td> <td>    4.434</td> <td> 0.000</td> <td>    4.334</td> <td>   11.201</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_26</th>           <td>    8.1892</td> <td>    1.841</td> <td>    4.448</td> <td> 0.000</td> <td>    4.580</td> <td>   11.798</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_27</th>           <td>    9.1430</td> <td>    1.921</td> <td>    4.760</td> <td> 0.000</td> <td>    5.378</td> <td>   12.908</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_28</th>           <td>    9.1452</td> <td>    1.599</td> <td>    5.718</td> <td> 0.000</td> <td>    6.011</td> <td>   12.280</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_29</th>           <td>    9.6527</td> <td>    1.737</td> <td>    5.558</td> <td> 0.000</td> <td>    6.249</td> <td>   13.057</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_30</th>           <td>    9.3181</td> <td>    1.728</td> <td>    5.393</td> <td> 0.000</td> <td>    5.932</td> <td>   12.704</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_31</th>           <td>    9.1197</td> <td>    1.709</td> <td>    5.338</td> <td> 0.000</td> <td>    5.771</td> <td>   12.469</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_32</th>           <td>    8.7717</td> <td>    2.140</td> <td>    4.098</td> <td> 0.000</td> <td>    4.577</td> <td>   12.966</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_33</th>           <td>    8.8289</td> <td>    1.934</td> <td>    4.565</td> <td> 0.000</td> <td>    5.038</td> <td>   12.619</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_34</th>           <td>   10.0379</td> <td>    1.837</td> <td>    5.464</td> <td> 0.000</td> <td>    6.437</td> <td>   13.638</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_35</th>           <td>   10.4543</td> <td>    1.854</td> <td>    5.640</td> <td> 0.000</td> <td>    6.821</td> <td>   14.087</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_36</th>           <td>    9.8504</td> <td>    1.971</td> <td>    4.997</td> <td> 0.000</td> <td>    5.987</td> <td>   13.714</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_37</th>           <td>    9.7406</td> <td>    1.939</td> <td>    5.025</td> <td> 0.000</td> <td>    5.941</td> <td>   13.540</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_38</th>           <td>    9.8777</td> <td>    2.038</td> <td>    4.847</td> <td> 0.000</td> <td>    5.883</td> <td>   13.872</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_39</th>           <td>    9.8544</td> <td>    2.150</td> <td>    4.583</td> <td> 0.000</td> <td>    5.640</td> <td>   14.068</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_40</th>           <td>    9.1128</td> <td>    2.555</td> <td>    3.567</td> <td> 0.000</td> <td>    4.106</td> <td>   14.120</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_41</th>           <td>    9.8220</td> <td>    2.150</td> <td>    4.568</td> <td> 0.000</td> <td>    5.608</td> <td>   14.036</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_42</th>           <td>    9.7010</td> <td>    2.296</td> <td>    4.225</td> <td> 0.000</td> <td>    5.200</td> <td>   14.202</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_43</th>           <td>   10.4936</td> <td>    2.357</td> <td>    4.452</td> <td> 0.000</td> <td>    5.874</td> <td>   15.113</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_44</th>           <td>   10.6271</td> <td>    2.314</td> <td>    4.593</td> <td> 0.000</td> <td>    6.092</td> <td>   15.162</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_45</th>           <td>   10.4365</td> <td>    2.517</td> <td>    4.147</td> <td> 0.000</td> <td>    5.504</td> <td>   15.369</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_46</th>           <td>    9.9541</td> <td>    2.363</td> <td>    4.212</td> <td> 0.000</td> <td>    5.322</td> <td>   14.586</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_47</th>           <td>   10.0544</td> <td>    2.509</td> <td>    4.007</td> <td> 0.000</td> <td>    5.136</td> <td>   14.973</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_48</th>           <td>   10.2497</td> <td>    2.226</td> <td>    4.604</td> <td> 0.000</td> <td>    5.886</td> <td>   14.613</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_49</th>           <td>   11.3670</td> <td>    2.507</td> <td>    4.533</td> <td> 0.000</td> <td>    6.453</td> <td>   16.281</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_50</th>           <td>   10.8615</td> <td>    2.431</td> <td>    4.468</td> <td> 0.000</td> <td>    6.097</td> <td>   15.626</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_51</th>           <td>   11.5437</td> <td>    2.628</td> <td>    4.392</td> <td> 0.000</td> <td>    6.392</td> <td>   16.695</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_52</th>           <td>   10.8473</td> <td>    2.611</td> <td>    4.154</td> <td> 0.000</td> <td>    5.729</td> <td>   15.965</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_53</th>           <td>   10.4790</td> <td>    2.587</td> <td>    4.051</td> <td> 0.000</td> <td>    5.409</td> <td>   15.549</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_54</th>           <td>   10.4557</td> <td>    2.970</td> <td>    3.521</td> <td> 0.000</td> <td>    4.635</td> <td>   16.276</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_55</th>           <td>   10.9216</td> <td>    2.511</td> <td>    4.350</td> <td> 0.000</td> <td>    6.001</td> <td>   15.842</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_56</th>           <td>   10.8460</td> <td>    2.451</td> <td>    4.424</td> <td> 0.000</td> <td>    6.041</td> <td>   15.651</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_57</th>           <td>   10.9921</td> <td>    2.372</td> <td>    4.635</td> <td> 0.000</td> <td>    6.344</td> <td>   15.640</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_58</th>           <td>   10.8387</td> <td>    2.504</td> <td>    4.328</td> <td> 0.000</td> <td>    5.930</td> <td>   15.747</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_59</th>           <td>   11.1572</td> <td>    2.390</td> <td>    4.668</td> <td> 0.000</td> <td>    6.472</td> <td>   15.842</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_60</th>           <td>   10.5086</td> <td>    2.411</td> <td>    4.358</td> <td> 0.000</td> <td>    5.783</td> <td>   15.234</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_61</th>           <td>    9.9075</td> <td>    2.534</td> <td>    3.910</td> <td> 0.000</td> <td>    4.941</td> <td>   14.874</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_62</th>           <td>   10.3922</td> <td>    2.550</td> <td>    4.075</td> <td> 0.000</td> <td>    5.394</td> <td>   15.390</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_63</th>           <td>   11.7017</td> <td>    2.812</td> <td>    4.162</td> <td> 0.000</td> <td>    6.191</td> <td>   17.213</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_64</th>           <td>    8.6192</td> <td>    1.415</td> <td>    6.090</td> <td> 0.000</td> <td>    5.845</td> <td>   11.393</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>1295448.559</td> <th>  Durbin-Watson:     </th>   <td>   1.978</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>   <td> 0.000</td>    <th>  Jarque-Bera (JB):  </th> <td>146858825.241</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>            <td> 6.106</td>    <th>  Prob(JB):          </th>   <td>    0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>        <td>57.689</td>    <th>  Cond. No.          </th>   <td>    38.5</td>   \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors are robust to cluster correlation (cluster)"
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &     distance     & \\textbf{  R-squared:         } &       0.021    \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &       0.021    \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &         nan    \\\\\n",
       "\\textbf{Date:}             & Wed, 21 May 2025 & \\textbf{  Prob (F-statistic):} &        nan     \\\\\n",
       "\\textbf{Time:}             &     12:50:37     & \\textbf{  Log-Likelihood:    } &  -6.0681e+06   \\\\\n",
       "\\textbf{No. Observations:} &     1122466      & \\textbf{  AIC:               } &   1.214e+07    \\\\\n",
       "\\textbf{Df Residuals:}     &     1122386      & \\textbf{  BIC:               } &   1.214e+07    \\\\\n",
       "\\textbf{Df Model:}         &          79      & \\textbf{                     } &                \\\\\n",
       "\\textbf{Covariance Type:}  &     cluster      & \\textbf{                     } &                \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                        & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{C(strike)[8]}   &       6.3997  &        1.581     &     4.048  &         0.000        &        3.301    &        9.498     \\\\\n",
       "\\textbf{C(strike)[9]}   &      11.1285  &        1.471     &     7.565  &         0.000        &        8.245    &       14.011     \\\\\n",
       "\\textbf{C(strike)[27]}  &      32.3514  &        1.487     &    21.751  &         0.000        &       29.436    &       35.267     \\\\\n",
       "\\textbf{C(strike)[32]}  &      21.5649  &        1.578     &    13.666  &         0.000        &       18.472    &       24.658     \\\\\n",
       "\\textbf{C(strike)[36]}  &      11.3384  &        1.555     &     7.291  &         0.000        &        8.290    &       14.386     \\\\\n",
       "\\textbf{C(strike)[42]}  &      21.3559  &        1.564     &    13.659  &         0.000        &       18.292    &       24.420     \\\\\n",
       "\\textbf{C(strike)[51]}  &      62.4166  &        1.522     &    41.001  &         0.000        &       59.433    &       65.400     \\\\\n",
       "\\textbf{C(strike)[56]}  &      29.9473  &        1.442     &    20.769  &         0.000        &       27.121    &       32.773     \\\\\n",
       "\\textbf{C(strike)[59]}  &      19.2128  &        1.589     &    12.091  &         0.000        &       16.098    &       22.327     \\\\\n",
       "\\textbf{C(strike)[60]}  &      24.5247  &        1.394     &    17.594  &         0.000        &       21.793    &       27.257     \\\\\n",
       "\\textbf{C(strike)[68]}  &      24.8234  &        1.388     &    17.889  &         0.000        &       22.104    &       27.543     \\\\\n",
       "\\textbf{C(strike)[70]}  &      41.0733  &        1.532     &    26.807  &         0.000        &       38.070    &       44.076     \\\\\n",
       "\\textbf{C(strike)[75]}  &       7.3207  &        1.567     &     4.671  &         0.000        &        4.249    &       10.393     \\\\\n",
       "\\textbf{C(strike)[90]}  &       7.3334  &        1.655     &     4.431  &         0.000        &        4.090    &       10.577     \\\\\n",
       "\\textbf{C(strike)[92]}  &      22.1585  &        1.464     &    15.132  &         0.000        &       19.288    &       25.028     \\\\\n",
       "\\textbf{C(strike)[96]}  &       6.2069  &        1.565     &     3.965  &         0.000        &        3.139    &        9.275     \\\\\n",
       "\\textbf{C(strike)[100]} &      94.5132  &        1.504     &    62.830  &         0.000        &       91.565    &       97.461     \\\\\n",
       "\\textbf{X\\_1}           &       0.0860  &        0.818     &     0.105  &         0.916        &       -1.517    &        1.689     \\\\\n",
       "\\textbf{X\\_2}           &       0.1569  &        0.460     &     0.341  &         0.733        &       -0.744    &        1.058     \\\\\n",
       "\\textbf{X\\_3}           &      -0.1503  &        0.539     &    -0.279  &         0.780        &       -1.207    &        0.906     \\\\\n",
       "\\textbf{X\\_4}           &      -0.4466  &        0.535     &    -0.835  &         0.404        &       -1.495    &        0.602     \\\\\n",
       "\\textbf{X\\_5}           &      -0.3552  &        0.422     &    -0.843  &         0.399        &       -1.182    &        0.471     \\\\\n",
       "\\textbf{X\\_7}           &       0.2063  &        0.391     &     0.527  &         0.598        &       -0.561    &        0.973     \\\\\n",
       "\\textbf{X\\_8}           &       0.4735  &        0.963     &     0.491  &         0.623        &       -1.415    &        2.362     \\\\\n",
       "\\textbf{X\\_9}           &       1.3909  &        0.567     &     2.452  &         0.014        &        0.279    &        2.503     \\\\\n",
       "\\textbf{X\\_10}          &       2.2659  &        0.692     &     3.275  &         0.001        &        0.910    &        3.622     \\\\\n",
       "\\textbf{X\\_11}          &       3.0489  &        1.078     &     2.829  &         0.005        &        0.936    &        5.161     \\\\\n",
       "\\textbf{X\\_12}          &       2.9647  &        0.974     &     3.044  &         0.002        &        1.056    &        4.874     \\\\\n",
       "\\textbf{X\\_13}          &       3.9978  &        1.057     &     3.783  &         0.000        &        1.926    &        6.069     \\\\\n",
       "\\textbf{X\\_14}          &       4.4349  &        1.199     &     3.699  &         0.000        &        2.085    &        6.784     \\\\\n",
       "\\textbf{X\\_15}          &       4.7951  &        1.442     &     3.325  &         0.001        &        1.969    &        7.621     \\\\\n",
       "\\textbf{X\\_16}          &       4.6474  &        1.119     &     4.151  &         0.000        &        2.453    &        6.842     \\\\\n",
       "\\textbf{X\\_17}          &       5.4441  &        1.632     &     3.337  &         0.001        &        2.246    &        8.642     \\\\\n",
       "\\textbf{X\\_18}          &       5.0650  &        1.578     &     3.209  &         0.001        &        1.971    &        8.159     \\\\\n",
       "\\textbf{X\\_19}          &       4.9594  &        1.217     &     4.076  &         0.000        &        2.575    &        7.344     \\\\\n",
       "\\textbf{X\\_20}          &       5.9651  &        1.636     &     3.645  &         0.000        &        2.758    &        9.172     \\\\\n",
       "\\textbf{X\\_21}          &       6.0250  &        0.995     &     6.056  &         0.000        &        4.075    &        7.975     \\\\\n",
       "\\textbf{X\\_22}          &       6.6108  &        1.178     &     5.614  &         0.000        &        4.303    &        8.919     \\\\\n",
       "\\textbf{X\\_23}          &       7.5220  &        1.477     &     5.094  &         0.000        &        4.628    &       10.416     \\\\\n",
       "\\textbf{X\\_24}          &       7.6613  &        1.681     &     4.557  &         0.000        &        4.366    &       10.956     \\\\\n",
       "\\textbf{X\\_25}          &       7.7675  &        1.752     &     4.434  &         0.000        &        4.334    &       11.201     \\\\\n",
       "\\textbf{X\\_26}          &       8.1892  &        1.841     &     4.448  &         0.000        &        4.580    &       11.798     \\\\\n",
       "\\textbf{X\\_27}          &       9.1430  &        1.921     &     4.760  &         0.000        &        5.378    &       12.908     \\\\\n",
       "\\textbf{X\\_28}          &       9.1452  &        1.599     &     5.718  &         0.000        &        6.011    &       12.280     \\\\\n",
       "\\textbf{X\\_29}          &       9.6527  &        1.737     &     5.558  &         0.000        &        6.249    &       13.057     \\\\\n",
       "\\textbf{X\\_30}          &       9.3181  &        1.728     &     5.393  &         0.000        &        5.932    &       12.704     \\\\\n",
       "\\textbf{X\\_31}          &       9.1197  &        1.709     &     5.338  &         0.000        &        5.771    &       12.469     \\\\\n",
       "\\textbf{X\\_32}          &       8.7717  &        2.140     &     4.098  &         0.000        &        4.577    &       12.966     \\\\\n",
       "\\textbf{X\\_33}          &       8.8289  &        1.934     &     4.565  &         0.000        &        5.038    &       12.619     \\\\\n",
       "\\textbf{X\\_34}          &      10.0379  &        1.837     &     5.464  &         0.000        &        6.437    &       13.638     \\\\\n",
       "\\textbf{X\\_35}          &      10.4543  &        1.854     &     5.640  &         0.000        &        6.821    &       14.087     \\\\\n",
       "\\textbf{X\\_36}          &       9.8504  &        1.971     &     4.997  &         0.000        &        5.987    &       13.714     \\\\\n",
       "\\textbf{X\\_37}          &       9.7406  &        1.939     &     5.025  &         0.000        &        5.941    &       13.540     \\\\\n",
       "\\textbf{X\\_38}          &       9.8777  &        2.038     &     4.847  &         0.000        &        5.883    &       13.872     \\\\\n",
       "\\textbf{X\\_39}          &       9.8544  &        2.150     &     4.583  &         0.000        &        5.640    &       14.068     \\\\\n",
       "\\textbf{X\\_40}          &       9.1128  &        2.555     &     3.567  &         0.000        &        4.106    &       14.120     \\\\\n",
       "\\textbf{X\\_41}          &       9.8220  &        2.150     &     4.568  &         0.000        &        5.608    &       14.036     \\\\\n",
       "\\textbf{X\\_42}          &       9.7010  &        2.296     &     4.225  &         0.000        &        5.200    &       14.202     \\\\\n",
       "\\textbf{X\\_43}          &      10.4936  &        2.357     &     4.452  &         0.000        &        5.874    &       15.113     \\\\\n",
       "\\textbf{X\\_44}          &      10.6271  &        2.314     &     4.593  &         0.000        &        6.092    &       15.162     \\\\\n",
       "\\textbf{X\\_45}          &      10.4365  &        2.517     &     4.147  &         0.000        &        5.504    &       15.369     \\\\\n",
       "\\textbf{X\\_46}          &       9.9541  &        2.363     &     4.212  &         0.000        &        5.322    &       14.586     \\\\\n",
       "\\textbf{X\\_47}          &      10.0544  &        2.509     &     4.007  &         0.000        &        5.136    &       14.973     \\\\\n",
       "\\textbf{X\\_48}          &      10.2497  &        2.226     &     4.604  &         0.000        &        5.886    &       14.613     \\\\\n",
       "\\textbf{X\\_49}          &      11.3670  &        2.507     &     4.533  &         0.000        &        6.453    &       16.281     \\\\\n",
       "\\textbf{X\\_50}          &      10.8615  &        2.431     &     4.468  &         0.000        &        6.097    &       15.626     \\\\\n",
       "\\textbf{X\\_51}          &      11.5437  &        2.628     &     4.392  &         0.000        &        6.392    &       16.695     \\\\\n",
       "\\textbf{X\\_52}          &      10.8473  &        2.611     &     4.154  &         0.000        &        5.729    &       15.965     \\\\\n",
       "\\textbf{X\\_53}          &      10.4790  &        2.587     &     4.051  &         0.000        &        5.409    &       15.549     \\\\\n",
       "\\textbf{X\\_54}          &      10.4557  &        2.970     &     3.521  &         0.000        &        4.635    &       16.276     \\\\\n",
       "\\textbf{X\\_55}          &      10.9216  &        2.511     &     4.350  &         0.000        &        6.001    &       15.842     \\\\\n",
       "\\textbf{X\\_56}          &      10.8460  &        2.451     &     4.424  &         0.000        &        6.041    &       15.651     \\\\\n",
       "\\textbf{X\\_57}          &      10.9921  &        2.372     &     4.635  &         0.000        &        6.344    &       15.640     \\\\\n",
       "\\textbf{X\\_58}          &      10.8387  &        2.504     &     4.328  &         0.000        &        5.930    &       15.747     \\\\\n",
       "\\textbf{X\\_59}          &      11.1572  &        2.390     &     4.668  &         0.000        &        6.472    &       15.842     \\\\\n",
       "\\textbf{X\\_60}          &      10.5086  &        2.411     &     4.358  &         0.000        &        5.783    &       15.234     \\\\\n",
       "\\textbf{X\\_61}          &       9.9075  &        2.534     &     3.910  &         0.000        &        4.941    &       14.874     \\\\\n",
       "\\textbf{X\\_62}          &      10.3922  &        2.550     &     4.075  &         0.000        &        5.394    &       15.390     \\\\\n",
       "\\textbf{X\\_63}          &      11.7017  &        2.812     &     4.162  &         0.000        &        6.191    &       17.213     \\\\\n",
       "\\textbf{X\\_64}          &       8.6192  &        1.415     &     6.090  &         0.000        &        5.845    &       11.393     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 1295448.559 & \\textbf{  Durbin-Watson:     } &       1.978    \\\\\n",
       "\\textbf{Prob(Omnibus):} &     0.000   & \\textbf{  Jarque-Bera (JB):  } & 146858825.241  \\\\\n",
       "\\textbf{Skew:}          &     6.106   & \\textbf{  Prob(JB):          } &        0.00    \\\\\n",
       "\\textbf{Kurtosis:}      &    57.689   & \\textbf{  Cond. No.          } &        38.5    \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors are robust to cluster correlation (cluster)"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:               distance   R-squared:                       0.021\n",
       "Model:                            OLS   Adj. R-squared:                  0.021\n",
       "Method:                 Least Squares   F-statistic:                       nan\n",
       "Date:                Wed, 21 May 2025   Prob (F-statistic):                nan\n",
       "Time:                        12:50:37   Log-Likelihood:            -6.0681e+06\n",
       "No. Observations:             1122466   AIC:                         1.214e+07\n",
       "Df Residuals:                 1122386   BIC:                         1.214e+07\n",
       "Df Model:                          79                                         \n",
       "Covariance Type:              cluster                                         \n",
       "==================================================================================\n",
       "                     coef    std err          z      P>|z|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------\n",
       "C(strike)[8]       6.3997      1.581      4.048      0.000       3.301       9.498\n",
       "C(strike)[9]      11.1285      1.471      7.565      0.000       8.245      14.011\n",
       "C(strike)[27]     32.3514      1.487     21.751      0.000      29.436      35.267\n",
       "C(strike)[32]     21.5649      1.578     13.666      0.000      18.472      24.658\n",
       "C(strike)[36]     11.3384      1.555      7.291      0.000       8.290      14.386\n",
       "C(strike)[42]     21.3559      1.564     13.659      0.000      18.292      24.420\n",
       "C(strike)[51]     62.4166      1.522     41.001      0.000      59.433      65.400\n",
       "C(strike)[56]     29.9473      1.442     20.769      0.000      27.121      32.773\n",
       "C(strike)[59]     19.2128      1.589     12.091      0.000      16.098      22.327\n",
       "C(strike)[60]     24.5247      1.394     17.594      0.000      21.793      27.257\n",
       "C(strike)[68]     24.8234      1.388     17.889      0.000      22.104      27.543\n",
       "C(strike)[70]     41.0733      1.532     26.807      0.000      38.070      44.076\n",
       "C(strike)[75]      7.3207      1.567      4.671      0.000       4.249      10.393\n",
       "C(strike)[90]      7.3334      1.655      4.431      0.000       4.090      10.577\n",
       "C(strike)[92]     22.1585      1.464     15.132      0.000      19.288      25.028\n",
       "C(strike)[96]      6.2069      1.565      3.965      0.000       3.139       9.275\n",
       "C(strike)[100]    94.5132      1.504     62.830      0.000      91.565      97.461\n",
       "X_1                0.0860      0.818      0.105      0.916      -1.517       1.689\n",
       "X_2                0.1569      0.460      0.341      0.733      -0.744       1.058\n",
       "X_3               -0.1503      0.539     -0.279      0.780      -1.207       0.906\n",
       "X_4               -0.4466      0.535     -0.835      0.404      -1.495       0.602\n",
       "X_5               -0.3552      0.422     -0.843      0.399      -1.182       0.471\n",
       "X_7                0.2063      0.391      0.527      0.598      -0.561       0.973\n",
       "X_8                0.4735      0.963      0.491      0.623      -1.415       2.362\n",
       "X_9                1.3909      0.567      2.452      0.014       0.279       2.503\n",
       "X_10               2.2659      0.692      3.275      0.001       0.910       3.622\n",
       "X_11               3.0489      1.078      2.829      0.005       0.936       5.161\n",
       "X_12               2.9647      0.974      3.044      0.002       1.056       4.874\n",
       "X_13               3.9978      1.057      3.783      0.000       1.926       6.069\n",
       "X_14               4.4349      1.199      3.699      0.000       2.085       6.784\n",
       "X_15               4.7951      1.442      3.325      0.001       1.969       7.621\n",
       "X_16               4.6474      1.119      4.151      0.000       2.453       6.842\n",
       "X_17               5.4441      1.632      3.337      0.001       2.246       8.642\n",
       "X_18               5.0650      1.578      3.209      0.001       1.971       8.159\n",
       "X_19               4.9594      1.217      4.076      0.000       2.575       7.344\n",
       "X_20               5.9651      1.636      3.645      0.000       2.758       9.172\n",
       "X_21               6.0250      0.995      6.056      0.000       4.075       7.975\n",
       "X_22               6.6108      1.178      5.614      0.000       4.303       8.919\n",
       "X_23               7.5220      1.477      5.094      0.000       4.628      10.416\n",
       "X_24               7.6613      1.681      4.557      0.000       4.366      10.956\n",
       "X_25               7.7675      1.752      4.434      0.000       4.334      11.201\n",
       "X_26               8.1892      1.841      4.448      0.000       4.580      11.798\n",
       "X_27               9.1430      1.921      4.760      0.000       5.378      12.908\n",
       "X_28               9.1452      1.599      5.718      0.000       6.011      12.280\n",
       "X_29               9.6527      1.737      5.558      0.000       6.249      13.057\n",
       "X_30               9.3181      1.728      5.393      0.000       5.932      12.704\n",
       "X_31               9.1197      1.709      5.338      0.000       5.771      12.469\n",
       "X_32               8.7717      2.140      4.098      0.000       4.577      12.966\n",
       "X_33               8.8289      1.934      4.565      0.000       5.038      12.619\n",
       "X_34              10.0379      1.837      5.464      0.000       6.437      13.638\n",
       "X_35              10.4543      1.854      5.640      0.000       6.821      14.087\n",
       "X_36               9.8504      1.971      4.997      0.000       5.987      13.714\n",
       "X_37               9.7406      1.939      5.025      0.000       5.941      13.540\n",
       "X_38               9.8777      2.038      4.847      0.000       5.883      13.872\n",
       "X_39               9.8544      2.150      4.583      0.000       5.640      14.068\n",
       "X_40               9.1128      2.555      3.567      0.000       4.106      14.120\n",
       "X_41               9.8220      2.150      4.568      0.000       5.608      14.036\n",
       "X_42               9.7010      2.296      4.225      0.000       5.200      14.202\n",
       "X_43              10.4936      2.357      4.452      0.000       5.874      15.113\n",
       "X_44              10.6271      2.314      4.593      0.000       6.092      15.162\n",
       "X_45              10.4365      2.517      4.147      0.000       5.504      15.369\n",
       "X_46               9.9541      2.363      4.212      0.000       5.322      14.586\n",
       "X_47              10.0544      2.509      4.007      0.000       5.136      14.973\n",
       "X_48              10.2497      2.226      4.604      0.000       5.886      14.613\n",
       "X_49              11.3670      2.507      4.533      0.000       6.453      16.281\n",
       "X_50              10.8615      2.431      4.468      0.000       6.097      15.626\n",
       "X_51              11.5437      2.628      4.392      0.000       6.392      16.695\n",
       "X_52              10.8473      2.611      4.154      0.000       5.729      15.965\n",
       "X_53              10.4790      2.587      4.051      0.000       5.409      15.549\n",
       "X_54              10.4557      2.970      3.521      0.000       4.635      16.276\n",
       "X_55              10.9216      2.511      4.350      0.000       6.001      15.842\n",
       "X_56              10.8460      2.451      4.424      0.000       6.041      15.651\n",
       "X_57              10.9921      2.372      4.635      0.000       6.344      15.640\n",
       "X_58              10.8387      2.504      4.328      0.000       5.930      15.747\n",
       "X_59              11.1572      2.390      4.668      0.000       6.472      15.842\n",
       "X_60              10.5086      2.411      4.358      0.000       5.783      15.234\n",
       "X_61               9.9075      2.534      3.910      0.000       4.941      14.874\n",
       "X_62              10.3922      2.550      4.075      0.000       5.394      15.390\n",
       "X_63              11.7017      2.812      4.162      0.000       6.191      17.213\n",
       "X_64               8.6192      1.415      6.090      0.000       5.845      11.393\n",
       "==============================================================================\n",
       "Omnibus:                  1295448.559   Durbin-Watson:                   1.978\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):        146858825.241\n",
       "Skew:                           6.106   Prob(JB):                         0.00\n",
       "Kurtosis:                      57.689   Cond. No.                         38.5\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors are robust to cluster correlation (cluster)\n",
       "\"\"\""
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# run the event study regression for distance around strikes\n",
    "# 7 lags and 56 leads\n",
    "# time vars provide the parameters of interest\n",
    "# drop the day = -2 variable for difference in means and to avoid multicollinearity\n",
    "# use strike fixed effects and cluster SE by strike\n",
    "reg = sm.ols('distance ~ ' + var_form + '  + C(strike) - 1', \n",
    "             data=df_high_ranking).fit(cov_type='cluster',\n",
    "                          cov_kwds={'groups': np.array(df_high_ranking[['strike']])})\n",
    "\n",
    "\n",
    "# save parameters and confidence intervals for plotting\n",
    "conf_int = reg.conf_int()\n",
    "res_highranking = pd.DataFrame({\n",
    "    'params': reg.params,\n",
    "    'ci_l': conf_int[0],  # lower bound\n",
    "    'ci_h': conf_int[1],  # upper bound\n",
    "})\n",
    "\n",
    "# print regression summary\n",
    "reg.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>distance</td>     <th>  R-squared:         </th>  <td>   0.027</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.027</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>     nan</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 21 May 2025</td> <th>  Prob (F-statistic):</th>   <td>   nan</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>12:51:52</td>     <th>  Log-Likelihood:    </th> <td>-1.0344e+07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>1826611</td>     <th>  AIC:               </th>  <td>2.069e+07</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>1826491</td>     <th>  BIC:               </th>  <td>2.069e+07</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>   119</td>      <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>       <td>cluster</td>     <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>           <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[1]</th>   <td>   15.1346</td> <td>    1.676</td> <td>    9.030</td> <td> 0.000</td> <td>   11.850</td> <td>   18.420</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[3]</th>   <td>   12.7875</td> <td>    1.726</td> <td>    7.408</td> <td> 0.000</td> <td>    9.404</td> <td>   16.171</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[4]</th>   <td>    8.1731</td> <td>    1.688</td> <td>    4.843</td> <td> 0.000</td> <td>    4.865</td> <td>   11.481</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[5]</th>   <td>   24.4193</td> <td>    1.756</td> <td>   13.904</td> <td> 0.000</td> <td>   20.977</td> <td>   27.862</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[6]</th>   <td>   24.3491</td> <td>    1.768</td> <td>   13.773</td> <td> 0.000</td> <td>   20.884</td> <td>   27.814</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[7]</th>   <td>   41.1649</td> <td>    1.629</td> <td>   25.275</td> <td> 0.000</td> <td>   37.973</td> <td>   44.357</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[10]</th>  <td>   15.9876</td> <td>    1.540</td> <td>   10.380</td> <td> 0.000</td> <td>   12.969</td> <td>   19.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[11]</th>  <td>   12.2181</td> <td>    1.486</td> <td>    8.223</td> <td> 0.000</td> <td>    9.306</td> <td>   15.130</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[12]</th>  <td>   11.5459</td> <td>    1.991</td> <td>    5.799</td> <td> 0.000</td> <td>    7.644</td> <td>   15.448</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[14]</th>  <td>   20.0092</td> <td>    1.681</td> <td>   11.900</td> <td> 0.000</td> <td>   16.714</td> <td>   23.305</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[15]</th>  <td>   12.1885</td> <td>    0.876</td> <td>   13.908</td> <td> 0.000</td> <td>   10.471</td> <td>   13.906</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[17]</th>  <td>   20.8669</td> <td>    1.589</td> <td>   13.129</td> <td> 0.000</td> <td>   17.752</td> <td>   23.982</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[19]</th>  <td>   22.9675</td> <td>    1.402</td> <td>   16.379</td> <td> 0.000</td> <td>   20.219</td> <td>   25.716</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[20]</th>  <td>   26.0504</td> <td>    1.502</td> <td>   17.344</td> <td> 0.000</td> <td>   23.107</td> <td>   28.994</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[24]</th>  <td>   59.9237</td> <td>    1.671</td> <td>   35.861</td> <td> 0.000</td> <td>   56.649</td> <td>   63.199</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[26]</th>  <td>   28.6322</td> <td>    1.648</td> <td>   17.370</td> <td> 0.000</td> <td>   25.401</td> <td>   31.863</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[28]</th>  <td>   20.8259</td> <td>    1.493</td> <td>   13.950</td> <td> 0.000</td> <td>   17.900</td> <td>   23.752</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[31]</th>  <td>   10.9431</td> <td>    1.838</td> <td>    5.952</td> <td> 0.000</td> <td>    7.340</td> <td>   14.546</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[33]</th>  <td>   13.6132</td> <td>    1.724</td> <td>    7.896</td> <td> 0.000</td> <td>   10.234</td> <td>   16.992</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[34]</th>  <td>   11.5264</td> <td>    1.730</td> <td>    6.661</td> <td> 0.000</td> <td>    8.135</td> <td>   14.918</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[35]</th>  <td>   10.4964</td> <td>    1.692</td> <td>    6.203</td> <td> 0.000</td> <td>    7.180</td> <td>   13.813</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[37]</th>  <td>    6.4563</td> <td>    1.726</td> <td>    3.741</td> <td> 0.000</td> <td>    3.074</td> <td>    9.839</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[38]</th>  <td>    7.8039</td> <td>    1.623</td> <td>    4.809</td> <td> 0.000</td> <td>    4.623</td> <td>   10.985</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[39]</th>  <td>    8.1718</td> <td>    1.699</td> <td>    4.811</td> <td> 0.000</td> <td>    4.843</td> <td>   11.501</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[40]</th>  <td>   28.2121</td> <td>    1.781</td> <td>   15.843</td> <td> 0.000</td> <td>   24.722</td> <td>   31.702</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[41]</th>  <td>    9.4462</td> <td>    1.729</td> <td>    5.465</td> <td> 0.000</td> <td>    6.058</td> <td>   12.834</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[43]</th>  <td>   20.7001</td> <td>    1.551</td> <td>   13.344</td> <td> 0.000</td> <td>   17.660</td> <td>   23.740</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[45]</th>  <td>   96.1260</td> <td>    1.556</td> <td>   61.788</td> <td> 0.000</td> <td>   93.077</td> <td>   99.175</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[47]</th>  <td>   13.5936</td> <td>    1.722</td> <td>    7.896</td> <td> 0.000</td> <td>   10.219</td> <td>   16.968</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[48]</th>  <td>   23.6450</td> <td>    1.712</td> <td>   13.809</td> <td> 0.000</td> <td>   20.289</td> <td>   27.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[49]</th>  <td>   16.4991</td> <td>    1.729</td> <td>    9.540</td> <td> 0.000</td> <td>   13.110</td> <td>   19.889</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[50]</th>  <td>   14.1431</td> <td>    1.761</td> <td>    8.031</td> <td> 0.000</td> <td>   10.691</td> <td>   17.595</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[54]</th>  <td>   20.5575</td> <td>    1.758</td> <td>   11.694</td> <td> 0.000</td> <td>   17.112</td> <td>   24.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[57]</th>  <td>   15.3967</td> <td>    1.748</td> <td>    8.806</td> <td> 0.000</td> <td>   11.970</td> <td>   18.823</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[58]</th>  <td>   24.2885</td> <td>    1.572</td> <td>   15.446</td> <td> 0.000</td> <td>   21.206</td> <td>   27.370</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[61]</th>  <td>   24.2343</td> <td>    1.684</td> <td>   14.392</td> <td> 0.000</td> <td>   20.934</td> <td>   27.535</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[62]</th>  <td>   43.4888</td> <td>    1.698</td> <td>   25.614</td> <td> 0.000</td> <td>   40.161</td> <td>   46.816</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[64]</th>  <td>   21.0346</td> <td>    1.706</td> <td>   12.330</td> <td> 0.000</td> <td>   17.691</td> <td>   24.378</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[65]</th>  <td>   59.5750</td> <td>    1.669</td> <td>   35.702</td> <td> 0.000</td> <td>   56.304</td> <td>   62.845</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[67]</th>  <td>   18.9726</td> <td>    1.525</td> <td>   12.442</td> <td> 0.000</td> <td>   15.984</td> <td>   21.961</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[69]</th>  <td>   20.6878</td> <td>    1.516</td> <td>   13.648</td> <td> 0.000</td> <td>   17.717</td> <td>   23.659</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[71]</th>  <td>   69.1600</td> <td>    1.642</td> <td>   42.130</td> <td> 0.000</td> <td>   65.943</td> <td>   72.377</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[72]</th>  <td>   14.6078</td> <td>    1.758</td> <td>    8.311</td> <td> 0.000</td> <td>   11.163</td> <td>   18.053</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[76]</th>  <td>   32.5924</td> <td>    1.631</td> <td>   19.979</td> <td> 0.000</td> <td>   29.395</td> <td>   35.790</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[77]</th>  <td>   25.5170</td> <td>    1.752</td> <td>   14.562</td> <td> 0.000</td> <td>   22.083</td> <td>   28.952</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[78]</th>  <td>   21.4598</td> <td>    1.692</td> <td>   12.687</td> <td> 0.000</td> <td>   18.145</td> <td>   24.775</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[81]</th>  <td>   33.8462</td> <td>    1.666</td> <td>   20.313</td> <td> 0.000</td> <td>   30.580</td> <td>   37.112</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[82]</th>  <td>   37.4108</td> <td>    1.504</td> <td>   24.869</td> <td> 0.000</td> <td>   34.462</td> <td>   40.359</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[83]</th>  <td>   26.4587</td> <td>    1.461</td> <td>   18.112</td> <td> 0.000</td> <td>   23.596</td> <td>   29.322</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[85]</th>  <td>   35.6189</td> <td>    1.529</td> <td>   23.290</td> <td> 0.000</td> <td>   32.621</td> <td>   38.616</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[87]</th>  <td>    9.1925</td> <td>    1.283</td> <td>    7.167</td> <td> 0.000</td> <td>    6.679</td> <td>   11.706</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[95]</th>  <td>   61.4285</td> <td>    1.123</td> <td>   54.701</td> <td> 0.000</td> <td>   59.228</td> <td>   63.630</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[97]</th>  <td>   28.1201</td> <td>    1.798</td> <td>   15.641</td> <td> 0.000</td> <td>   24.596</td> <td>   31.644</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[101]</th> <td>    4.9830</td> <td>    1.778</td> <td>    2.803</td> <td> 0.005</td> <td>    1.498</td> <td>    8.468</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[102]</th> <td>   24.8645</td> <td>    1.787</td> <td>   13.917</td> <td> 0.000</td> <td>   21.363</td> <td>   28.366</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[106]</th> <td>    7.5739</td> <td>    0.690</td> <td>   10.979</td> <td> 0.000</td> <td>    6.222</td> <td>    8.926</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(strike)[107]</th> <td>   50.8564</td> <td>    0.643</td> <td>   79.123</td> <td> 0.000</td> <td>   49.597</td> <td>   52.116</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_1</th>            <td>   -1.6827</td> <td>    1.428</td> <td>   -1.179</td> <td> 0.239</td> <td>   -4.481</td> <td>    1.115</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_2</th>            <td>   -0.2308</td> <td>    1.115</td> <td>   -0.207</td> <td> 0.836</td> <td>   -2.416</td> <td>    1.954</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_3</th>            <td>   -1.2055</td> <td>    1.307</td> <td>   -0.922</td> <td> 0.356</td> <td>   -3.768</td> <td>    1.357</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_4</th>            <td>   -1.2577</td> <td>    1.246</td> <td>   -1.010</td> <td> 0.313</td> <td>   -3.699</td> <td>    1.183</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_5</th>            <td>   -0.4295</td> <td>    0.744</td> <td>   -0.577</td> <td> 0.564</td> <td>   -1.888</td> <td>    1.029</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_7</th>            <td>    0.6396</td> <td>    0.718</td> <td>    0.891</td> <td> 0.373</td> <td>   -0.768</td> <td>    2.047</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_8</th>            <td>   -0.7294</td> <td>    0.612</td> <td>   -1.191</td> <td> 0.233</td> <td>   -1.929</td> <td>    0.470</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_9</th>            <td>    2.7060</td> <td>    0.839</td> <td>    3.225</td> <td> 0.001</td> <td>    1.061</td> <td>    4.351</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_10</th>           <td>    3.2679</td> <td>    0.803</td> <td>    4.072</td> <td> 0.000</td> <td>    1.695</td> <td>    4.841</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_11</th>           <td>    4.6278</td> <td>    0.808</td> <td>    5.730</td> <td> 0.000</td> <td>    3.045</td> <td>    6.211</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_12</th>           <td>    5.1440</td> <td>    0.913</td> <td>    5.637</td> <td> 0.000</td> <td>    3.356</td> <td>    6.933</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_13</th>           <td>    5.0577</td> <td>    1.108</td> <td>    4.566</td> <td> 0.000</td> <td>    2.886</td> <td>    7.229</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_14</th>           <td>    6.5986</td> <td>    1.072</td> <td>    6.155</td> <td> 0.000</td> <td>    4.497</td> <td>    8.700</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_15</th>           <td>    5.9555</td> <td>    1.069</td> <td>    5.571</td> <td> 0.000</td> <td>    3.860</td> <td>    8.051</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_16</th>           <td>    6.7672</td> <td>    1.133</td> <td>    5.975</td> <td> 0.000</td> <td>    4.548</td> <td>    8.987</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_17</th>           <td>    8.4765</td> <td>    1.524</td> <td>    5.562</td> <td> 0.000</td> <td>    5.490</td> <td>   11.463</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_18</th>           <td>    8.1046</td> <td>    1.316</td> <td>    6.160</td> <td> 0.000</td> <td>    5.526</td> <td>   10.683</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_19</th>           <td>    7.2059</td> <td>    1.243</td> <td>    5.797</td> <td> 0.000</td> <td>    4.770</td> <td>    9.642</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_20</th>           <td>    7.6858</td> <td>    1.443</td> <td>    5.327</td> <td> 0.000</td> <td>    4.858</td> <td>   10.514</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_21</th>           <td>    9.0450</td> <td>    1.528</td> <td>    5.918</td> <td> 0.000</td> <td>    6.049</td> <td>   12.041</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_22</th>           <td>    9.7428</td> <td>    1.455</td> <td>    6.694</td> <td> 0.000</td> <td>    6.890</td> <td>   12.595</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_23</th>           <td>    9.1037</td> <td>    1.483</td> <td>    6.140</td> <td> 0.000</td> <td>    6.198</td> <td>   12.010</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_24</th>           <td>   10.6320</td> <td>    1.868</td> <td>    5.691</td> <td> 0.000</td> <td>    6.971</td> <td>   14.293</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_25</th>           <td>   10.0925</td> <td>    1.639</td> <td>    6.158</td> <td> 0.000</td> <td>    6.880</td> <td>   13.305</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_26</th>           <td>   10.3537</td> <td>    1.899</td> <td>    5.453</td> <td> 0.000</td> <td>    6.633</td> <td>   14.075</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_27</th>           <td>   10.0662</td> <td>    1.966</td> <td>    5.121</td> <td> 0.000</td> <td>    6.214</td> <td>   13.919</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_28</th>           <td>   10.8504</td> <td>    1.834</td> <td>    5.916</td> <td> 0.000</td> <td>    7.256</td> <td>   14.445</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_29</th>           <td>   12.3229</td> <td>    1.885</td> <td>    6.537</td> <td> 0.000</td> <td>    8.628</td> <td>   16.018</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_30</th>           <td>   11.5412</td> <td>    1.780</td> <td>    6.484</td> <td> 0.000</td> <td>    8.052</td> <td>   15.030</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_31</th>           <td>   11.1704</td> <td>    1.860</td> <td>    6.006</td> <td> 0.000</td> <td>    7.525</td> <td>   14.816</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_32</th>           <td>   11.2935</td> <td>    1.994</td> <td>    5.665</td> <td> 0.000</td> <td>    7.386</td> <td>   15.201</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_33</th>           <td>   11.3151</td> <td>    1.950</td> <td>    5.802</td> <td> 0.000</td> <td>    7.493</td> <td>   15.138</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_34</th>           <td>   12.1552</td> <td>    2.395</td> <td>    5.075</td> <td> 0.000</td> <td>    7.461</td> <td>   16.850</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_35</th>           <td>   12.1166</td> <td>    2.210</td> <td>    5.483</td> <td> 0.000</td> <td>    7.785</td> <td>   16.448</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_36</th>           <td>   12.1905</td> <td>    2.058</td> <td>    5.924</td> <td> 0.000</td> <td>    8.157</td> <td>   16.224</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_37</th>           <td>   13.4675</td> <td>    2.458</td> <td>    5.479</td> <td> 0.000</td> <td>    8.649</td> <td>   18.285</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_38</th>           <td>   12.6190</td> <td>    2.544</td> <td>    4.960</td> <td> 0.000</td> <td>    7.633</td> <td>   17.605</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_39</th>           <td>   13.6512</td> <td>    2.690</td> <td>    5.075</td> <td> 0.000</td> <td>    8.380</td> <td>   18.923</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_40</th>           <td>   13.4233</td> <td>    2.916</td> <td>    4.603</td> <td> 0.000</td> <td>    7.708</td> <td>   19.139</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_41</th>           <td>   12.8036</td> <td>    2.795</td> <td>    4.581</td> <td> 0.000</td> <td>    7.326</td> <td>   18.281</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_42</th>           <td>   14.2960</td> <td>    2.891</td> <td>    4.945</td> <td> 0.000</td> <td>    8.629</td> <td>   19.963</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_43</th>           <td>   13.8688</td> <td>    2.620</td> <td>    5.294</td> <td> 0.000</td> <td>    8.734</td> <td>   19.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_44</th>           <td>   13.4454</td> <td>    2.382</td> <td>    5.644</td> <td> 0.000</td> <td>    8.776</td> <td>   18.114</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_45</th>           <td>   15.3327</td> <td>    2.970</td> <td>    5.162</td> <td> 0.000</td> <td>    9.511</td> <td>   21.154</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_46</th>           <td>   14.5537</td> <td>    2.780</td> <td>    5.236</td> <td> 0.000</td> <td>    9.106</td> <td>   20.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_47</th>           <td>   15.5439</td> <td>    3.069</td> <td>    5.065</td> <td> 0.000</td> <td>    9.529</td> <td>   21.559</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_48</th>           <td>   14.5518</td> <td>    3.138</td> <td>    4.637</td> <td> 0.000</td> <td>    8.401</td> <td>   20.702</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_49</th>           <td>   14.7364</td> <td>    2.842</td> <td>    5.186</td> <td> 0.000</td> <td>    9.167</td> <td>   20.306</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_50</th>           <td>   14.5933</td> <td>    2.662</td> <td>    5.482</td> <td> 0.000</td> <td>    9.376</td> <td>   19.811</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_51</th>           <td>   14.6800</td> <td>    2.570</td> <td>    5.712</td> <td> 0.000</td> <td>    9.643</td> <td>   19.717</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_52</th>           <td>   15.1376</td> <td>    2.996</td> <td>    5.052</td> <td> 0.000</td> <td>    9.265</td> <td>   21.010</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_53</th>           <td>   14.9319</td> <td>    3.028</td> <td>    4.931</td> <td> 0.000</td> <td>    8.997</td> <td>   20.867</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_54</th>           <td>   14.3907</td> <td>    3.175</td> <td>    4.533</td> <td> 0.000</td> <td>    8.169</td> <td>   20.613</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_55</th>           <td>   14.6140</td> <td>    3.011</td> <td>    4.854</td> <td> 0.000</td> <td>    8.713</td> <td>   20.515</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_56</th>           <td>   15.0359</td> <td>    3.050</td> <td>    4.930</td> <td> 0.000</td> <td>    9.059</td> <td>   21.013</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_57</th>           <td>   14.0007</td> <td>    2.871</td> <td>    4.877</td> <td> 0.000</td> <td>    8.374</td> <td>   19.628</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_58</th>           <td>   14.4931</td> <td>    3.073</td> <td>    4.716</td> <td> 0.000</td> <td>    8.470</td> <td>   20.516</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_59</th>           <td>   13.8685</td> <td>    3.184</td> <td>    4.356</td> <td> 0.000</td> <td>    7.628</td> <td>   20.109</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_60</th>           <td>   14.3694</td> <td>    3.722</td> <td>    3.861</td> <td> 0.000</td> <td>    7.074</td> <td>   21.665</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_61</th>           <td>   12.3704</td> <td>    3.509</td> <td>    3.525</td> <td> 0.000</td> <td>    5.492</td> <td>   19.248</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_62</th>           <td>   14.4119</td> <td>    3.774</td> <td>    3.819</td> <td> 0.000</td> <td>    7.016</td> <td>   21.808</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_63</th>           <td>   13.3295</td> <td>    3.304</td> <td>    4.035</td> <td> 0.000</td> <td>    6.854</td> <td>   19.805</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>X_64</th>           <td>   15.1118</td> <td>    3.819</td> <td>    3.957</td> <td> 0.000</td> <td>    7.627</td> <td>   22.597</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>1860605.462</td> <th>  Durbin-Watson:     </th>   <td>   1.975</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>   <td> 0.000</td>    <th>  Jarque-Bera (JB):  </th> <td>108105630.652</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>            <td> 5.155</td>    <th>  Prob(JB):          </th>   <td>    0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>        <td>39.250</td>    <th>  Cond. No.          </th>   <td>    41.9</td>   \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors are robust to cluster correlation (cluster)"
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &     distance     & \\textbf{  R-squared:         } &       0.027    \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &       0.027    \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &         nan    \\\\\n",
       "\\textbf{Date:}             & Wed, 21 May 2025 & \\textbf{  Prob (F-statistic):} &        nan     \\\\\n",
       "\\textbf{Time:}             &     12:51:52     & \\textbf{  Log-Likelihood:    } &  -1.0344e+07   \\\\\n",
       "\\textbf{No. Observations:} &     1826611      & \\textbf{  AIC:               } &   2.069e+07    \\\\\n",
       "\\textbf{Df Residuals:}     &     1826491      & \\textbf{  BIC:               } &   2.069e+07    \\\\\n",
       "\\textbf{Df Model:}         &         119      & \\textbf{                     } &                \\\\\n",
       "\\textbf{Covariance Type:}  &     cluster      & \\textbf{                     } &                \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                        & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{C(strike)[1]}   &      15.1346  &        1.676     &     9.030  &         0.000        &       11.850    &       18.420     \\\\\n",
       "\\textbf{C(strike)[3]}   &      12.7875  &        1.726     &     7.408  &         0.000        &        9.404    &       16.171     \\\\\n",
       "\\textbf{C(strike)[4]}   &       8.1731  &        1.688     &     4.843  &         0.000        &        4.865    &       11.481     \\\\\n",
       "\\textbf{C(strike)[5]}   &      24.4193  &        1.756     &    13.904  &         0.000        &       20.977    &       27.862     \\\\\n",
       "\\textbf{C(strike)[6]}   &      24.3491  &        1.768     &    13.773  &         0.000        &       20.884    &       27.814     \\\\\n",
       "\\textbf{C(strike)[7]}   &      41.1649  &        1.629     &    25.275  &         0.000        &       37.973    &       44.357     \\\\\n",
       "\\textbf{C(strike)[10]}  &      15.9876  &        1.540     &    10.380  &         0.000        &       12.969    &       19.006     \\\\\n",
       "\\textbf{C(strike)[11]}  &      12.2181  &        1.486     &     8.223  &         0.000        &        9.306    &       15.130     \\\\\n",
       "\\textbf{C(strike)[12]}  &      11.5459  &        1.991     &     5.799  &         0.000        &        7.644    &       15.448     \\\\\n",
       "\\textbf{C(strike)[14]}  &      20.0092  &        1.681     &    11.900  &         0.000        &       16.714    &       23.305     \\\\\n",
       "\\textbf{C(strike)[15]}  &      12.1885  &        0.876     &    13.908  &         0.000        &       10.471    &       13.906     \\\\\n",
       "\\textbf{C(strike)[17]}  &      20.8669  &        1.589     &    13.129  &         0.000        &       17.752    &       23.982     \\\\\n",
       "\\textbf{C(strike)[19]}  &      22.9675  &        1.402     &    16.379  &         0.000        &       20.219    &       25.716     \\\\\n",
       "\\textbf{C(strike)[20]}  &      26.0504  &        1.502     &    17.344  &         0.000        &       23.107    &       28.994     \\\\\n",
       "\\textbf{C(strike)[24]}  &      59.9237  &        1.671     &    35.861  &         0.000        &       56.649    &       63.199     \\\\\n",
       "\\textbf{C(strike)[26]}  &      28.6322  &        1.648     &    17.370  &         0.000        &       25.401    &       31.863     \\\\\n",
       "\\textbf{C(strike)[28]}  &      20.8259  &        1.493     &    13.950  &         0.000        &       17.900    &       23.752     \\\\\n",
       "\\textbf{C(strike)[31]}  &      10.9431  &        1.838     &     5.952  &         0.000        &        7.340    &       14.546     \\\\\n",
       "\\textbf{C(strike)[33]}  &      13.6132  &        1.724     &     7.896  &         0.000        &       10.234    &       16.992     \\\\\n",
       "\\textbf{C(strike)[34]}  &      11.5264  &        1.730     &     6.661  &         0.000        &        8.135    &       14.918     \\\\\n",
       "\\textbf{C(strike)[35]}  &      10.4964  &        1.692     &     6.203  &         0.000        &        7.180    &       13.813     \\\\\n",
       "\\textbf{C(strike)[37]}  &       6.4563  &        1.726     &     3.741  &         0.000        &        3.074    &        9.839     \\\\\n",
       "\\textbf{C(strike)[38]}  &       7.8039  &        1.623     &     4.809  &         0.000        &        4.623    &       10.985     \\\\\n",
       "\\textbf{C(strike)[39]}  &       8.1718  &        1.699     &     4.811  &         0.000        &        4.843    &       11.501     \\\\\n",
       "\\textbf{C(strike)[40]}  &      28.2121  &        1.781     &    15.843  &         0.000        &       24.722    &       31.702     \\\\\n",
       "\\textbf{C(strike)[41]}  &       9.4462  &        1.729     &     5.465  &         0.000        &        6.058    &       12.834     \\\\\n",
       "\\textbf{C(strike)[43]}  &      20.7001  &        1.551     &    13.344  &         0.000        &       17.660    &       23.740     \\\\\n",
       "\\textbf{C(strike)[45]}  &      96.1260  &        1.556     &    61.788  &         0.000        &       93.077    &       99.175     \\\\\n",
       "\\textbf{C(strike)[47]}  &      13.5936  &        1.722     &     7.896  &         0.000        &       10.219    &       16.968     \\\\\n",
       "\\textbf{C(strike)[48]}  &      23.6450  &        1.712     &    13.809  &         0.000        &       20.289    &       27.001     \\\\\n",
       "\\textbf{C(strike)[49]}  &      16.4991  &        1.729     &     9.540  &         0.000        &       13.110    &       19.889     \\\\\n",
       "\\textbf{C(strike)[50]}  &      14.1431  &        1.761     &     8.031  &         0.000        &       10.691    &       17.595     \\\\\n",
       "\\textbf{C(strike)[54]}  &      20.5575  &        1.758     &    11.694  &         0.000        &       17.112    &       24.003     \\\\\n",
       "\\textbf{C(strike)[57]}  &      15.3967  &        1.748     &     8.806  &         0.000        &       11.970    &       18.823     \\\\\n",
       "\\textbf{C(strike)[58]}  &      24.2885  &        1.572     &    15.446  &         0.000        &       21.206    &       27.370     \\\\\n",
       "\\textbf{C(strike)[61]}  &      24.2343  &        1.684     &    14.392  &         0.000        &       20.934    &       27.535     \\\\\n",
       "\\textbf{C(strike)[62]}  &      43.4888  &        1.698     &    25.614  &         0.000        &       40.161    &       46.816     \\\\\n",
       "\\textbf{C(strike)[64]}  &      21.0346  &        1.706     &    12.330  &         0.000        &       17.691    &       24.378     \\\\\n",
       "\\textbf{C(strike)[65]}  &      59.5750  &        1.669     &    35.702  &         0.000        &       56.304    &       62.845     \\\\\n",
       "\\textbf{C(strike)[67]}  &      18.9726  &        1.525     &    12.442  &         0.000        &       15.984    &       21.961     \\\\\n",
       "\\textbf{C(strike)[69]}  &      20.6878  &        1.516     &    13.648  &         0.000        &       17.717    &       23.659     \\\\\n",
       "\\textbf{C(strike)[71]}  &      69.1600  &        1.642     &    42.130  &         0.000        &       65.943    &       72.377     \\\\\n",
       "\\textbf{C(strike)[72]}  &      14.6078  &        1.758     &     8.311  &         0.000        &       11.163    &       18.053     \\\\\n",
       "\\textbf{C(strike)[76]}  &      32.5924  &        1.631     &    19.979  &         0.000        &       29.395    &       35.790     \\\\\n",
       "\\textbf{C(strike)[77]}  &      25.5170  &        1.752     &    14.562  &         0.000        &       22.083    &       28.952     \\\\\n",
       "\\textbf{C(strike)[78]}  &      21.4598  &        1.692     &    12.687  &         0.000        &       18.145    &       24.775     \\\\\n",
       "\\textbf{C(strike)[81]}  &      33.8462  &        1.666     &    20.313  &         0.000        &       30.580    &       37.112     \\\\\n",
       "\\textbf{C(strike)[82]}  &      37.4108  &        1.504     &    24.869  &         0.000        &       34.462    &       40.359     \\\\\n",
       "\\textbf{C(strike)[83]}  &      26.4587  &        1.461     &    18.112  &         0.000        &       23.596    &       29.322     \\\\\n",
       "\\textbf{C(strike)[85]}  &      35.6189  &        1.529     &    23.290  &         0.000        &       32.621    &       38.616     \\\\\n",
       "\\textbf{C(strike)[87]}  &       9.1925  &        1.283     &     7.167  &         0.000        &        6.679    &       11.706     \\\\\n",
       "\\textbf{C(strike)[95]}  &      61.4285  &        1.123     &    54.701  &         0.000        &       59.228    &       63.630     \\\\\n",
       "\\textbf{C(strike)[97]}  &      28.1201  &        1.798     &    15.641  &         0.000        &       24.596    &       31.644     \\\\\n",
       "\\textbf{C(strike)[101]} &       4.9830  &        1.778     &     2.803  &         0.005        &        1.498    &        8.468     \\\\\n",
       "\\textbf{C(strike)[102]} &      24.8645  &        1.787     &    13.917  &         0.000        &       21.363    &       28.366     \\\\\n",
       "\\textbf{C(strike)[106]} &       7.5739  &        0.690     &    10.979  &         0.000        &        6.222    &        8.926     \\\\\n",
       "\\textbf{C(strike)[107]} &      50.8564  &        0.643     &    79.123  &         0.000        &       49.597    &       52.116     \\\\\n",
       "\\textbf{X\\_1}           &      -1.6827  &        1.428     &    -1.179  &         0.239        &       -4.481    &        1.115     \\\\\n",
       "\\textbf{X\\_2}           &      -0.2308  &        1.115     &    -0.207  &         0.836        &       -2.416    &        1.954     \\\\\n",
       "\\textbf{X\\_3}           &      -1.2055  &        1.307     &    -0.922  &         0.356        &       -3.768    &        1.357     \\\\\n",
       "\\textbf{X\\_4}           &      -1.2577  &        1.246     &    -1.010  &         0.313        &       -3.699    &        1.183     \\\\\n",
       "\\textbf{X\\_5}           &      -0.4295  &        0.744     &    -0.577  &         0.564        &       -1.888    &        1.029     \\\\\n",
       "\\textbf{X\\_7}           &       0.6396  &        0.718     &     0.891  &         0.373        &       -0.768    &        2.047     \\\\\n",
       "\\textbf{X\\_8}           &      -0.7294  &        0.612     &    -1.191  &         0.233        &       -1.929    &        0.470     \\\\\n",
       "\\textbf{X\\_9}           &       2.7060  &        0.839     &     3.225  &         0.001        &        1.061    &        4.351     \\\\\n",
       "\\textbf{X\\_10}          &       3.2679  &        0.803     &     4.072  &         0.000        &        1.695    &        4.841     \\\\\n",
       "\\textbf{X\\_11}          &       4.6278  &        0.808     &     5.730  &         0.000        &        3.045    &        6.211     \\\\\n",
       "\\textbf{X\\_12}          &       5.1440  &        0.913     &     5.637  &         0.000        &        3.356    &        6.933     \\\\\n",
       "\\textbf{X\\_13}          &       5.0577  &        1.108     &     4.566  &         0.000        &        2.886    &        7.229     \\\\\n",
       "\\textbf{X\\_14}          &       6.5986  &        1.072     &     6.155  &         0.000        &        4.497    &        8.700     \\\\\n",
       "\\textbf{X\\_15}          &       5.9555  &        1.069     &     5.571  &         0.000        &        3.860    &        8.051     \\\\\n",
       "\\textbf{X\\_16}          &       6.7672  &        1.133     &     5.975  &         0.000        &        4.548    &        8.987     \\\\\n",
       "\\textbf{X\\_17}          &       8.4765  &        1.524     &     5.562  &         0.000        &        5.490    &       11.463     \\\\\n",
       "\\textbf{X\\_18}          &       8.1046  &        1.316     &     6.160  &         0.000        &        5.526    &       10.683     \\\\\n",
       "\\textbf{X\\_19}          &       7.2059  &        1.243     &     5.797  &         0.000        &        4.770    &        9.642     \\\\\n",
       "\\textbf{X\\_20}          &       7.6858  &        1.443     &     5.327  &         0.000        &        4.858    &       10.514     \\\\\n",
       "\\textbf{X\\_21}          &       9.0450  &        1.528     &     5.918  &         0.000        &        6.049    &       12.041     \\\\\n",
       "\\textbf{X\\_22}          &       9.7428  &        1.455     &     6.694  &         0.000        &        6.890    &       12.595     \\\\\n",
       "\\textbf{X\\_23}          &       9.1037  &        1.483     &     6.140  &         0.000        &        6.198    &       12.010     \\\\\n",
       "\\textbf{X\\_24}          &      10.6320  &        1.868     &     5.691  &         0.000        &        6.971    &       14.293     \\\\\n",
       "\\textbf{X\\_25}          &      10.0925  &        1.639     &     6.158  &         0.000        &        6.880    &       13.305     \\\\\n",
       "\\textbf{X\\_26}          &      10.3537  &        1.899     &     5.453  &         0.000        &        6.633    &       14.075     \\\\\n",
       "\\textbf{X\\_27}          &      10.0662  &        1.966     &     5.121  &         0.000        &        6.214    &       13.919     \\\\\n",
       "\\textbf{X\\_28}          &      10.8504  &        1.834     &     5.916  &         0.000        &        7.256    &       14.445     \\\\\n",
       "\\textbf{X\\_29}          &      12.3229  &        1.885     &     6.537  &         0.000        &        8.628    &       16.018     \\\\\n",
       "\\textbf{X\\_30}          &      11.5412  &        1.780     &     6.484  &         0.000        &        8.052    &       15.030     \\\\\n",
       "\\textbf{X\\_31}          &      11.1704  &        1.860     &     6.006  &         0.000        &        7.525    &       14.816     \\\\\n",
       "\\textbf{X\\_32}          &      11.2935  &        1.994     &     5.665  &         0.000        &        7.386    &       15.201     \\\\\n",
       "\\textbf{X\\_33}          &      11.3151  &        1.950     &     5.802  &         0.000        &        7.493    &       15.138     \\\\\n",
       "\\textbf{X\\_34}          &      12.1552  &        2.395     &     5.075  &         0.000        &        7.461    &       16.850     \\\\\n",
       "\\textbf{X\\_35}          &      12.1166  &        2.210     &     5.483  &         0.000        &        7.785    &       16.448     \\\\\n",
       "\\textbf{X\\_36}          &      12.1905  &        2.058     &     5.924  &         0.000        &        8.157    &       16.224     \\\\\n",
       "\\textbf{X\\_37}          &      13.4675  &        2.458     &     5.479  &         0.000        &        8.649    &       18.285     \\\\\n",
       "\\textbf{X\\_38}          &      12.6190  &        2.544     &     4.960  &         0.000        &        7.633    &       17.605     \\\\\n",
       "\\textbf{X\\_39}          &      13.6512  &        2.690     &     5.075  &         0.000        &        8.380    &       18.923     \\\\\n",
       "\\textbf{X\\_40}          &      13.4233  &        2.916     &     4.603  &         0.000        &        7.708    &       19.139     \\\\\n",
       "\\textbf{X\\_41}          &      12.8036  &        2.795     &     4.581  &         0.000        &        7.326    &       18.281     \\\\\n",
       "\\textbf{X\\_42}          &      14.2960  &        2.891     &     4.945  &         0.000        &        8.629    &       19.963     \\\\\n",
       "\\textbf{X\\_43}          &      13.8688  &        2.620     &     5.294  &         0.000        &        8.734    &       19.004     \\\\\n",
       "\\textbf{X\\_44}          &      13.4454  &        2.382     &     5.644  &         0.000        &        8.776    &       18.114     \\\\\n",
       "\\textbf{X\\_45}          &      15.3327  &        2.970     &     5.162  &         0.000        &        9.511    &       21.154     \\\\\n",
       "\\textbf{X\\_46}          &      14.5537  &        2.780     &     5.236  &         0.000        &        9.106    &       20.001     \\\\\n",
       "\\textbf{X\\_47}          &      15.5439  &        3.069     &     5.065  &         0.000        &        9.529    &       21.559     \\\\\n",
       "\\textbf{X\\_48}          &      14.5518  &        3.138     &     4.637  &         0.000        &        8.401    &       20.702     \\\\\n",
       "\\textbf{X\\_49}          &      14.7364  &        2.842     &     5.186  &         0.000        &        9.167    &       20.306     \\\\\n",
       "\\textbf{X\\_50}          &      14.5933  &        2.662     &     5.482  &         0.000        &        9.376    &       19.811     \\\\\n",
       "\\textbf{X\\_51}          &      14.6800  &        2.570     &     5.712  &         0.000        &        9.643    &       19.717     \\\\\n",
       "\\textbf{X\\_52}          &      15.1376  &        2.996     &     5.052  &         0.000        &        9.265    &       21.010     \\\\\n",
       "\\textbf{X\\_53}          &      14.9319  &        3.028     &     4.931  &         0.000        &        8.997    &       20.867     \\\\\n",
       "\\textbf{X\\_54}          &      14.3907  &        3.175     &     4.533  &         0.000        &        8.169    &       20.613     \\\\\n",
       "\\textbf{X\\_55}          &      14.6140  &        3.011     &     4.854  &         0.000        &        8.713    &       20.515     \\\\\n",
       "\\textbf{X\\_56}          &      15.0359  &        3.050     &     4.930  &         0.000        &        9.059    &       21.013     \\\\\n",
       "\\textbf{X\\_57}          &      14.0007  &        2.871     &     4.877  &         0.000        &        8.374    &       19.628     \\\\\n",
       "\\textbf{X\\_58}          &      14.4931  &        3.073     &     4.716  &         0.000        &        8.470    &       20.516     \\\\\n",
       "\\textbf{X\\_59}          &      13.8685  &        3.184     &     4.356  &         0.000        &        7.628    &       20.109     \\\\\n",
       "\\textbf{X\\_60}          &      14.3694  &        3.722     &     3.861  &         0.000        &        7.074    &       21.665     \\\\\n",
       "\\textbf{X\\_61}          &      12.3704  &        3.509     &     3.525  &         0.000        &        5.492    &       19.248     \\\\\n",
       "\\textbf{X\\_62}          &      14.4119  &        3.774     &     3.819  &         0.000        &        7.016    &       21.808     \\\\\n",
       "\\textbf{X\\_63}          &      13.3295  &        3.304     &     4.035  &         0.000        &        6.854    &       19.805     \\\\\n",
       "\\textbf{X\\_64}          &      15.1118  &        3.819     &     3.957  &         0.000        &        7.627    &       22.597     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 1860605.462 & \\textbf{  Durbin-Watson:     } &       1.975    \\\\\n",
       "\\textbf{Prob(Omnibus):} &     0.000   & \\textbf{  Jarque-Bera (JB):  } & 108105630.652  \\\\\n",
       "\\textbf{Skew:}          &     5.155   & \\textbf{  Prob(JB):          } &        0.00    \\\\\n",
       "\\textbf{Kurtosis:}      &    39.250   & \\textbf{  Cond. No.          } &        41.9    \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors are robust to cluster correlation (cluster)"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:               distance   R-squared:                       0.027\n",
       "Model:                            OLS   Adj. R-squared:                  0.027\n",
       "Method:                 Least Squares   F-statistic:                       nan\n",
       "Date:                Wed, 21 May 2025   Prob (F-statistic):                nan\n",
       "Time:                        12:51:52   Log-Likelihood:            -1.0344e+07\n",
       "No. Observations:             1826611   AIC:                         2.069e+07\n",
       "Df Residuals:                 1826491   BIC:                         2.069e+07\n",
       "Df Model:                         119                                         \n",
       "Covariance Type:              cluster                                         \n",
       "==================================================================================\n",
       "                     coef    std err          z      P>|z|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------\n",
       "C(strike)[1]      15.1346      1.676      9.030      0.000      11.850      18.420\n",
       "C(strike)[3]      12.7875      1.726      7.408      0.000       9.404      16.171\n",
       "C(strike)[4]       8.1731      1.688      4.843      0.000       4.865      11.481\n",
       "C(strike)[5]      24.4193      1.756     13.904      0.000      20.977      27.862\n",
       "C(strike)[6]      24.3491      1.768     13.773      0.000      20.884      27.814\n",
       "C(strike)[7]      41.1649      1.629     25.275      0.000      37.973      44.357\n",
       "C(strike)[10]     15.9876      1.540     10.380      0.000      12.969      19.006\n",
       "C(strike)[11]     12.2181      1.486      8.223      0.000       9.306      15.130\n",
       "C(strike)[12]     11.5459      1.991      5.799      0.000       7.644      15.448\n",
       "C(strike)[14]     20.0092      1.681     11.900      0.000      16.714      23.305\n",
       "C(strike)[15]     12.1885      0.876     13.908      0.000      10.471      13.906\n",
       "C(strike)[17]     20.8669      1.589     13.129      0.000      17.752      23.982\n",
       "C(strike)[19]     22.9675      1.402     16.379      0.000      20.219      25.716\n",
       "C(strike)[20]     26.0504      1.502     17.344      0.000      23.107      28.994\n",
       "C(strike)[24]     59.9237      1.671     35.861      0.000      56.649      63.199\n",
       "C(strike)[26]     28.6322      1.648     17.370      0.000      25.401      31.863\n",
       "C(strike)[28]     20.8259      1.493     13.950      0.000      17.900      23.752\n",
       "C(strike)[31]     10.9431      1.838      5.952      0.000       7.340      14.546\n",
       "C(strike)[33]     13.6132      1.724      7.896      0.000      10.234      16.992\n",
       "C(strike)[34]     11.5264      1.730      6.661      0.000       8.135      14.918\n",
       "C(strike)[35]     10.4964      1.692      6.203      0.000       7.180      13.813\n",
       "C(strike)[37]      6.4563      1.726      3.741      0.000       3.074       9.839\n",
       "C(strike)[38]      7.8039      1.623      4.809      0.000       4.623      10.985\n",
       "C(strike)[39]      8.1718      1.699      4.811      0.000       4.843      11.501\n",
       "C(strike)[40]     28.2121      1.781     15.843      0.000      24.722      31.702\n",
       "C(strike)[41]      9.4462      1.729      5.465      0.000       6.058      12.834\n",
       "C(strike)[43]     20.7001      1.551     13.344      0.000      17.660      23.740\n",
       "C(strike)[45]     96.1260      1.556     61.788      0.000      93.077      99.175\n",
       "C(strike)[47]     13.5936      1.722      7.896      0.000      10.219      16.968\n",
       "C(strike)[48]     23.6450      1.712     13.809      0.000      20.289      27.001\n",
       "C(strike)[49]     16.4991      1.729      9.540      0.000      13.110      19.889\n",
       "C(strike)[50]     14.1431      1.761      8.031      0.000      10.691      17.595\n",
       "C(strike)[54]     20.5575      1.758     11.694      0.000      17.112      24.003\n",
       "C(strike)[57]     15.3967      1.748      8.806      0.000      11.970      18.823\n",
       "C(strike)[58]     24.2885      1.572     15.446      0.000      21.206      27.370\n",
       "C(strike)[61]     24.2343      1.684     14.392      0.000      20.934      27.535\n",
       "C(strike)[62]     43.4888      1.698     25.614      0.000      40.161      46.816\n",
       "C(strike)[64]     21.0346      1.706     12.330      0.000      17.691      24.378\n",
       "C(strike)[65]     59.5750      1.669     35.702      0.000      56.304      62.845\n",
       "C(strike)[67]     18.9726      1.525     12.442      0.000      15.984      21.961\n",
       "C(strike)[69]     20.6878      1.516     13.648      0.000      17.717      23.659\n",
       "C(strike)[71]     69.1600      1.642     42.130      0.000      65.943      72.377\n",
       "C(strike)[72]     14.6078      1.758      8.311      0.000      11.163      18.053\n",
       "C(strike)[76]     32.5924      1.631     19.979      0.000      29.395      35.790\n",
       "C(strike)[77]     25.5170      1.752     14.562      0.000      22.083      28.952\n",
       "C(strike)[78]     21.4598      1.692     12.687      0.000      18.145      24.775\n",
       "C(strike)[81]     33.8462      1.666     20.313      0.000      30.580      37.112\n",
       "C(strike)[82]     37.4108      1.504     24.869      0.000      34.462      40.359\n",
       "C(strike)[83]     26.4587      1.461     18.112      0.000      23.596      29.322\n",
       "C(strike)[85]     35.6189      1.529     23.290      0.000      32.621      38.616\n",
       "C(strike)[87]      9.1925      1.283      7.167      0.000       6.679      11.706\n",
       "C(strike)[95]     61.4285      1.123     54.701      0.000      59.228      63.630\n",
       "C(strike)[97]     28.1201      1.798     15.641      0.000      24.596      31.644\n",
       "C(strike)[101]     4.9830      1.778      2.803      0.005       1.498       8.468\n",
       "C(strike)[102]    24.8645      1.787     13.917      0.000      21.363      28.366\n",
       "C(strike)[106]     7.5739      0.690     10.979      0.000       6.222       8.926\n",
       "C(strike)[107]    50.8564      0.643     79.123      0.000      49.597      52.116\n",
       "X_1               -1.6827      1.428     -1.179      0.239      -4.481       1.115\n",
       "X_2               -0.2308      1.115     -0.207      0.836      -2.416       1.954\n",
       "X_3               -1.2055      1.307     -0.922      0.356      -3.768       1.357\n",
       "X_4               -1.2577      1.246     -1.010      0.313      -3.699       1.183\n",
       "X_5               -0.4295      0.744     -0.577      0.564      -1.888       1.029\n",
       "X_7                0.6396      0.718      0.891      0.373      -0.768       2.047\n",
       "X_8               -0.7294      0.612     -1.191      0.233      -1.929       0.470\n",
       "X_9                2.7060      0.839      3.225      0.001       1.061       4.351\n",
       "X_10               3.2679      0.803      4.072      0.000       1.695       4.841\n",
       "X_11               4.6278      0.808      5.730      0.000       3.045       6.211\n",
       "X_12               5.1440      0.913      5.637      0.000       3.356       6.933\n",
       "X_13               5.0577      1.108      4.566      0.000       2.886       7.229\n",
       "X_14               6.5986      1.072      6.155      0.000       4.497       8.700\n",
       "X_15               5.9555      1.069      5.571      0.000       3.860       8.051\n",
       "X_16               6.7672      1.133      5.975      0.000       4.548       8.987\n",
       "X_17               8.4765      1.524      5.562      0.000       5.490      11.463\n",
       "X_18               8.1046      1.316      6.160      0.000       5.526      10.683\n",
       "X_19               7.2059      1.243      5.797      0.000       4.770       9.642\n",
       "X_20               7.6858      1.443      5.327      0.000       4.858      10.514\n",
       "X_21               9.0450      1.528      5.918      0.000       6.049      12.041\n",
       "X_22               9.7428      1.455      6.694      0.000       6.890      12.595\n",
       "X_23               9.1037      1.483      6.140      0.000       6.198      12.010\n",
       "X_24              10.6320      1.868      5.691      0.000       6.971      14.293\n",
       "X_25              10.0925      1.639      6.158      0.000       6.880      13.305\n",
       "X_26              10.3537      1.899      5.453      0.000       6.633      14.075\n",
       "X_27              10.0662      1.966      5.121      0.000       6.214      13.919\n",
       "X_28              10.8504      1.834      5.916      0.000       7.256      14.445\n",
       "X_29              12.3229      1.885      6.537      0.000       8.628      16.018\n",
       "X_30              11.5412      1.780      6.484      0.000       8.052      15.030\n",
       "X_31              11.1704      1.860      6.006      0.000       7.525      14.816\n",
       "X_32              11.2935      1.994      5.665      0.000       7.386      15.201\n",
       "X_33              11.3151      1.950      5.802      0.000       7.493      15.138\n",
       "X_34              12.1552      2.395      5.075      0.000       7.461      16.850\n",
       "X_35              12.1166      2.210      5.483      0.000       7.785      16.448\n",
       "X_36              12.1905      2.058      5.924      0.000       8.157      16.224\n",
       "X_37              13.4675      2.458      5.479      0.000       8.649      18.285\n",
       "X_38              12.6190      2.544      4.960      0.000       7.633      17.605\n",
       "X_39              13.6512      2.690      5.075      0.000       8.380      18.923\n",
       "X_40              13.4233      2.916      4.603      0.000       7.708      19.139\n",
       "X_41              12.8036      2.795      4.581      0.000       7.326      18.281\n",
       "X_42              14.2960      2.891      4.945      0.000       8.629      19.963\n",
       "X_43              13.8688      2.620      5.294      0.000       8.734      19.004\n",
       "X_44              13.4454      2.382      5.644      0.000       8.776      18.114\n",
       "X_45              15.3327      2.970      5.162      0.000       9.511      21.154\n",
       "X_46              14.5537      2.780      5.236      0.000       9.106      20.001\n",
       "X_47              15.5439      3.069      5.065      0.000       9.529      21.559\n",
       "X_48              14.5518      3.138      4.637      0.000       8.401      20.702\n",
       "X_49              14.7364      2.842      5.186      0.000       9.167      20.306\n",
       "X_50              14.5933      2.662      5.482      0.000       9.376      19.811\n",
       "X_51              14.6800      2.570      5.712      0.000       9.643      19.717\n",
       "X_52              15.1376      2.996      5.052      0.000       9.265      21.010\n",
       "X_53              14.9319      3.028      4.931      0.000       8.997      20.867\n",
       "X_54              14.3907      3.175      4.533      0.000       8.169      20.613\n",
       "X_55              14.6140      3.011      4.854      0.000       8.713      20.515\n",
       "X_56              15.0359      3.050      4.930      0.000       9.059      21.013\n",
       "X_57              14.0007      2.871      4.877      0.000       8.374      19.628\n",
       "X_58              14.4931      3.073      4.716      0.000       8.470      20.516\n",
       "X_59              13.8685      3.184      4.356      0.000       7.628      20.109\n",
       "X_60              14.3694      3.722      3.861      0.000       7.074      21.665\n",
       "X_61              12.3704      3.509      3.525      0.000       5.492      19.248\n",
       "X_62              14.4119      3.774      3.819      0.000       7.016      21.808\n",
       "X_63              13.3295      3.304      4.035      0.000       6.854      19.805\n",
       "X_64              15.1118      3.819      3.957      0.000       7.627      22.597\n",
       "==============================================================================\n",
       "Omnibus:                  1860605.462   Durbin-Watson:                   1.975\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):        108105630.652\n",
       "Skew:                           5.155   Prob(JB):                         0.00\n",
       "Kurtosis:                      39.250   Cond. No.                         41.9\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors are robust to cluster correlation (cluster)\n",
       "\"\"\""
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# run the event study regression for distance around strikes\n",
    "# 7 lags and 56 leads\n",
    "# time vars provide the parameters of interest\n",
    "# drop the day = -2 variable for difference in means and to avoid multicollinearity\n",
    "# use strike fixed effects and cluster SE by strike\n",
    "reg = sm.ols('distance ~ ' + var_form + '  + C(strike) - 1', \n",
    "             data=df_low_ranking).fit(cov_type='cluster',\n",
    "                          cov_kwds={'groups': np.array(df_low_ranking[['strike']])})\n",
    "\n",
    "\n",
    "# save parameters and confidence intervals for plotting\n",
    "conf_int = reg.conf_int()\n",
    "res_lowranking = pd.DataFrame({\n",
    "    'params': reg.params,\n",
    "    'ci_l': conf_int[0],  # lower bound\n",
    "    'ci_h': conf_int[1],  # upper bound\n",
    "})\n",
    "\n",
    "# print regression summary\n",
    "reg.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reindex the results to go from day = -7 to day = 56\n",
    "event_res_highranking = res_highranking[res_highranking.index.to_series().str.startswith(\"X_\")].copy()\n",
    "event_res_highranking = event_res_highranking.reset_index().rename(columns={'index': 'varname'})\n",
    "event_res_highranking['day'] = event_res_highranking['varname'].str.extract(r'X_(\\d+)').astype(float).astype('Int64') - 8\n",
    "\n",
    "event_res_lowranking = res_lowranking[res_lowranking.index.to_series().str.startswith(\"X_\")].copy()\n",
    "event_res_lowranking = event_res_lowranking.reset_index().rename(columns={'index': 'varname'})\n",
    "event_res_lowranking['day'] = event_res_lowranking['varname'].str.extract(r'X_(\\d+)').astype(float).astype('Int64') - 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_two_group(event_res_highranking, event_res_lowranking,\n",
    "                       label1='High Ranking Militants Killed',\n",
    "                       label2='No High Ranking Militants Killed',\n",
    "                       color1='C0', color2='C1',\n",
    "                       ylabel='Distance (km)',\n",
    "                       outfile='figures/figureX_distance_militantrank.pdf',\n",
    "                       ylim=[-7, 27], xlim=[-7, 56])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Figure S14B: Distance Results with Strike Quantile Adjustment (timing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# subset distance df by strike quantiles\n",
    "df_period1 = df[df['strike_quantile'] == 0]\n",
    "df_period2 = df[df['strike_quantile'] == 1]\n",
    "df_period3 = df[df['strike_quantile'] == 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# run the event study regression for distance around strikes\n",
    "# 7 lags and 56 leads\n",
    "# time vars provide the parameters of interest\n",
    "# drop the day = -2 variable for difference in means and to avoid multicollinearity\n",
    "# use strike fixed effects and cluster SE by strike\n",
    "reg = sm.ols('distance ~ ' + var_form + '  + C(strike) - 1', \n",
    "             data=df_period1).fit(cov_type='cluster',\n",
    "                          cov_kwds={'groups': np.array(df_period1[['strike']])})\n",
    "\n",
    "\n",
    "# save parameters and confidence intervals for plotting\n",
    "conf_int = reg.conf_int()\n",
    "res_period1 = pd.DataFrame({\n",
    "    'params': reg.params,\n",
    "    'ci_l': conf_int[0],  # lower bound\n",
    "    'ci_h': conf_int[1],  # upper bound\n",
    "})\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# run the event study regression for distance around strikes\n",
    "# 7 lags and 56 leads\n",
    "# time vars provide the parameters of interest\n",
    "# drop the day = -2 variable for difference in means and to avoid multicollinearity\n",
    "# use strike fixed effects and cluster SE by strike\n",
    "reg = sm.ols('distance ~ ' + var_form + '  + C(strike) - 1', \n",
    "             data=df_period2).fit(cov_type='cluster',\n",
    "                          cov_kwds={'groups': np.array(df_period2[['strike']])})\n",
    "\n",
    "\n",
    "# save parameters and confidence intervals for plotting\n",
    "conf_int = reg.conf_int()\n",
    "res_period2 = pd.DataFrame({\n",
    "    'params': reg.params,\n",
    "    'ci_l': conf_int[0],  # lower bound\n",
    "    'ci_h': conf_int[1],  # upper bound\n",
    "})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# run the event study regression for distance around strikes\n",
    "# 7 lags and 56 leads\n",
    "# time vars provide the parameters of interest\n",
    "# drop the day = -2 variable for difference in means and to avoid multicollinearity\n",
    "# use strike fixed effects and cluster SE by strike\n",
    "reg = sm.ols('distance ~ ' + var_form + '  + C(strike) - 1', \n",
    "             data=df_period3).fit(cov_type='cluster',\n",
    "                          cov_kwds={'groups': np.array(df_period3[['strike']])})\n",
    "\n",
    "\n",
    "# save parameters and confidence intervals for plotting\n",
    "conf_int = reg.conf_int()\n",
    "res_period3 = pd.DataFrame({\n",
    "    'params': reg.params,\n",
    "    'ci_l': conf_int[0],  # lower bound\n",
    "    'ci_h': conf_int[1],  # upper bound\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reindex the results to go from day = -7 to day = 56\n",
    "event_res_period1 = res_period1[res_period1.index.to_series().str.startswith(\"X_\")].copy()\n",
    "event_res_period1 = event_res_period1.reset_index().rename(columns={'index': 'varname'})\n",
    "event_res_period1['day'] = event_res_period1['varname'].str.extract(r'X_(\\d+)').astype(float).astype('Int64') - 8\n",
    "\n",
    "event_res_period2 = res_period2[res_period2.index.to_series().str.startswith(\"X_\")].copy()\n",
    "event_res_period2 = event_res_period2.reset_index().rename(columns={'index': 'varname'})\n",
    "event_res_period2['day'] = event_res_period2['varname'].str.extract(r'X_(\\d+)').astype(float).astype('Int64') - 8\n",
    "\n",
    "event_res_period3 = res_period3[res_period3.index.to_series().str.startswith(\"X_\")].copy()\n",
    "event_res_period3 = event_res_period3.reset_index().rename(columns={'index': 'varname'})\n",
    "event_res_period3['day'] = event_res_period3['varname'].str.extract(r'X_(\\d+)').astype(float).astype('Int64') - 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_three_group(event_res_period1, event_res_period2, event_res_period3,\n",
    "                       label1='Jan. 2010 -- Mar. 2012', label2='Mar. 2012 -- May 2012', label3='May 2012 -- Oct. 2012',\n",
    "                       color1='C0', color2='C1', color3='C2',\n",
    "                       ylabel='Distance (km)',\n",
    "                       outfile='figures/figureX_distance_epoch.pdf',\n",
    "                       ylim=[-7, 27], xlim=[-7, 56])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "drones",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
